{"id":116991,"date":"2026-10-22T10:07:26","date_gmt":"2026-10-22T02:07:26","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/116991.html"},"modified":"2026-10-22T10:07:26","modified_gmt":"2026-10-22T02:07:26","slug":"%e3%80%90%e5%85%b7%e8%ba%ab%e7%ac%94%e8%ae%b0%e3%80%91%e3%80%90piperpi0-5%e3%80%91%e4%b8%80%e3%80%81%e5%9f%ba%e4%ba%8e%e9%94%ae%e7%9b%98%e5%ae%9e%e7%8e%b0%e6%95%b0%e6%8d%ae%e9%87%87%e9%9b%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/116991.html","title":{"rendered":"\u3010\u5177\u8eab\u7b14\u8bb0\u3011\u3010Piper+PI0.5\u3011\u4e00\u3001\u57fa\u4e8e\u952e\u76d8\u5b9e\u73b0\u6570\u636e\u91c7\u96c6"},"content":{"rendered":"

\u6982\u8ff0\u4e0eQA<\/h3>\n

\u672c\u7cfb\u5217\u4e3a\u6211\u5728vla\u4e0a\u7684\u7b2c\u4e8c\u4e2a\u5b9e\u8df5：\u57fa\u4e8epiper\u673a\u68b0\u81c2\u5b8c\u6210\u6570\u636e\u91c7\u96c6–\u683c\u5f0f\u8f6c\u6362–pi05\u5fae\u8c03–\u8bc4\u4f30–panda\u652f\u7ebf\u5bf9\u6bd4\u3002\u672c\u6587\u4e3b\u8981\u4ecb\u7ecd\u5982\u4f55\u5b9e\u73b0\u57fa\u4e8e\u952e\u76d8\u9065\u64cd\u4f5c\u91c7\u96c6\u6570\u636e\u3002<\/p>\n

\n

1. \u5148\u9a8c\u8bc1\u81ea\u5df1\u7684\u8bbe\u5907\u80fd\u4e0d\u80fd\u652f\u6301\u952e\u76d8\u9065\u64cd<\/h3>\n

\u5176\u5b9e\u5728\u8fdb\u884c\u8fd9\u9879\u5b9e\u8df5\u6211\u4e00\u76f4\u5728\u62c5\u5fc3\u81ea\u5df1\u7684\u96c6\u663eubuntu\u7535\u8111\u65e0\u6cd5\u652f\u6301\u9065\u91c7，\u5fc5\u987b\u8981\u53bbautodl\u4e0a\u79df\u5361\u7136\u540e\u505a\u753b\u9762\u6e32\u67d3\u548c\u8f6c\u53d1\u624d\u80fd\u5b9e\u73b0\u3002\u8fdb\u4e00\u6b65\u4e86\u89e3\u540e\u624d\u77e5\u9053\u8fd9\u9879\u4e3b\u8981\u9700\u6c42\u7684\u662fCPU，\u663e\u5361\u53ea\u8981\u80fd\u5e26\u52a8mujoco\u5c31\u884c\u3002<\/p>\n

\u4e8e\u662f\u6211\u901a\u8fc7\u4e0b\u9762\u7684\u4ee3\u7801\u521d\u6b65\u5c1d\u8bd5\u4e86\u9065\u64cd\u4f5c，\u5e76\u6d4b\u8bd5\u4e86\u81ea\u5df1\u7535\u8111\u4e0a\u80fd\u505a\u5230\u7684fps：<\/p>\n

import argparse
\nimport time
\nfrom pathlib import Path
\n# \u7528\u4e8e\u7b97\u5747\u503c
\nfrom statistics import mean<\/p>\n

import numpy as np
\n# \u7528libero\u642d\u5efa\u4efb\u52a1\u5957\u4ef6\u73af\u5883
\nfrom libero.libero import benchmark, get_libero_path
\nfrom libero.libero.envs.env_wrapper import ControlEnv
\n# \u7528robosuite\u5b9a\u4e49\u952e\u76d8\u8f93\u5165\u8bbe\u5907\u548c\u52a8\u4f5c\u6620\u5c04
\nfrom robosuite.devices import Keyboard
\nfrom robosuite.utils.input_utils import input2action<\/p>\n

# \u5b9e\u73b0 python script.py –two-cameras，store_true\u8868\u793a\u547d\u4ee4\u884c\u53ea\u8981\u5199\u4e86\u8fd9\u4e2a\u53c2\u6570，\u5176\u503c\u5c31\u662ftrue，\u4e0d\u5199\u5c31\u662ffalse
\nparser = argparse.ArgumentParser()
\nparser.add_argument("–two-cameras", action="store_true")
\nargs = parser.parse_args()<\/p>\n

mode = "viewr + \u4e24\u8def\u76f8\u673a" if args.two_cameras else "\u4ec5 viewer"<\/p>\n

suite = benchmark.get_benchmark_dict()["libero_spatial"]()
\ntask = suite.get_task(0)
\n# \u7528Path\u62fc\u76ee\u5f55
\nbddl_path = (
\n Path(get_libero_path("bddl_files")) # \u7b2c 1 \u90e8\u5206：\u6839\u76ee\u5f55
\n \/ task.problem_folder # \u7b2c 2 \u90e8\u5206：\u4efb\u52a1\u5b50\u76ee\u5f55
\n \/ task.bddl_file # \u7b2c 3 \u90e8\u5206：BDDL \u6587\u4ef6\u540d
\n)<\/p>\n

print(f"\u4efb\u52a1: {task.language}")
\nprint(f"\u6d4b\u8bd5\u6a21\u5f0f: {mode}")<\/p>\n

env = ControlEnv(
\n bddl_file_name=str(bddl_path),
\n robots=["Panda"],
\n controller="OSC_POSE",
\n control_freq=20,
\n # \u6253\u5f00\u4e3b\u7a97\u53e3
\n has_renderer=True,
\n has_offscreen_renderer=True,
\n render_camera="agentview",
\n # \u662f\u5426\u5728\u89c2\u6d4bobs\u91cc\u8fd4\u56de\u56fe\u50cf，True\u7684\u8bddobs\u5c31\u6709agentview_image\u3001robot0_eye_in_hand_image\u56fe\u50cf
\n use_camera_obs=args.two_cameras,
\n camera_names=["agentview", "robot0_eye_in_hand"],
\n camera_widths=256,
\n camera_heights=256,
\n ignore_done=True,
\n)
\ndevice = None<\/p>\n

try:
\n env.seed(1000)
\n observation = env.reset()<\/p>\n

if args.two_cameras:
\n for key in ["agentview_image", "robot0_eye_in_hand_image"]:
\n print(key, observation[key].shape)
\n assert observation[key].shape == (256, 256, 3)<\/p>\n

# \u65e0\u79fb\u52a8，\u4fdd\u6301\u5939\u722a\u6253\u5f00
\n idle_action = np.array([0, 0, 0, 0, 0, 0, -1], dtype=float)
\n # \u9884\u70ed
\n for _ in range(20):
\n env.step(idle_action)
\n # \u628a\u5f53\u524d\u753b\u9762\u753b\u5230\u7a97\u53e3
\n env.env.render()
\n print("\u5f00\u59cb\u6d4bfps, \u4e0d\u9700\u6309\u952e")
\n fps_records = []<\/p>\n

# \u6bcf\u6b21\u6d4b 100 \u4e2a\u63a7\u5236\u6b65，\u5305\u62ec\u7269\u7406\u4eff\u771f\u3001\u89c2\u6d4b\u751f\u6210\u548c\u7a97\u53e3\u663e\u793a
\n # \u6b64\u5904\u4e0d sleep，\u6d4b\u7684\u662f\u5904\u7406\u80fd\u529b，\u4e0d\u662f\u4eba\u4e3a\u9650\u5236\u7684 20 FPS
\n for trial in range(3):
\n start = time.perf_counter()<\/p>\n

for _ in range(100):
\n env.step(idle_action)
\n env.env.render()<\/p>\n

elapsed = time.perf_counter() – start
\n fps = 100 \/ elapsed
\n fps_records.append(fps)<\/p>\n

print(
\n f"\u7b2c {trial + 1} \u6b21: "
\n f"100 \u6b65\u8017\u65f6={elapsed} \u79d2，FPS={fps}"
\n )<\/p>\n

print("\u6a21\u5f0f:", mode)
\n print("\u4e09\u6b21 FPS:", fps_records)
\n print("\u5e73\u5747 FPS:", mean(fps_records))<\/p>\n

# \u6d4b\u5b8c\u91cd\u65b0\u521d\u59cb\u5316，\u907f\u514d\u6d4b\u8bd5\u8fc7\u7a0b\u6539\u53d8\u573a\u666f\u72b6\u6001
\n env.reset()
\n env.env.render()<\/p>\n

device = Keyboard(
\n # \u4f4d\u7f6e\u7075\u654f\u5ea6\u548c\u59ff\u6001\u7075\u654f\u5ea6
\n pos_sensitivity=0.1,
\n rot_sensitivity=0.2
\n )
\n device.start_control()<\/p>\n

print("\\\\n\u5f00\u59cb\u952e\u76d8\u64cd\u4f5c:")
\n # \u8fd9\u4e2a\u5bf9\u5e94\u5173\u7cfb\u662frobosuite Keyboard\u63d0\u524d\u5b9a\u4e49\u597d\u7684
\n print("W\/A\/S\/D \u6c34\u5e73\u79fb\u52a8:R\/F \u5347\u964d；\u7a7a\u683c\u5f00\u5408\u5939\u722a\u3002")
\n print("\u672c\u811a\u672c\u6309 Q \u7ed3\u675f，\u4e5f\u53ef\u4ee5\u5728\u7ec8\u7aef\u6309 Ctrl+C\u3002")<\/p>\n

while True:
\n start = time.perf_counter()<\/p>\n

action, grasp = input2action(
\n device=device,
\n robot=env.robots[0],
\n active_arm="right",
\n )<\/p>\n

if action is None:
\n break<\/p>\n

assert action.shape == (7, )
\n env.step(action)
\n env.env.render()<\/p>\n

remaining = 1 \/ 20 – (time.perf_counter() – start)
\n if remaining > 0:
\n time.sleep(remaining)<\/p>\n

except KeyboardInterrupt:
\n print("\\\\n\u6d4b\u8bd5\u7ed3\u675f\u3002") <\/p>\n

finally:
\n if device is not None:
\n device.listener.stop()
\n env.close()<\/p>\n

\u6211\u89c9\u5f97\u8fd9\u91cc\u7684\u6838\u5fc3\u6709\u4e24\u70b9：<\/p>\n

1. \u642d\u573a\u666f，\u8ddf\u4e4b\u524dmujoco\u5728libero\u4e0a\u7684\u5b9e\u8df5\u4e00\u6837，\u5148\u627e\u5230\u9700\u6c42\u7684\u4efb\u52a1，\u5f97\u5230\u5176bddl\u8def\u5f84，\u901a\u8fc7libero\u642d\u5efa\u4eff\u771f\u573a\u666f<\/p>\n

2. \u4f7f\u7528 robosuite \u63d0\u4f9b\u7684\u952e\u76d8\u8f93\u5165\u5de5\u5177\u548c\u673a\u68b0\u81c2\u63a7\u5236\u5668，\u628a\u6309\u952e\u8f6c\u6362\u6210\u52a8\u4f5c，\u901a\u8fc7 env.step(action) \u9a71\u52a8\u673a\u68b0\u81c2\u3002robosuite \u5c01\u88c5\u4e86\u673a\u5668\u4eba\u3001\u4f20\u611f\u5668\u3001\u63a7\u5236\u5668\u7b49\u63a5\u53e3，\u65b9\u4fbf\u7528\u6237\u64cd\u4f5c MuJoCo \u4e2d\u7684\u673a\u5668\u4eba\u3002<\/p>\n

\u6b64\u5916，has_renderer\u8868\u793a\u8981\u5f00\u4e3b\u7a97\u53e3，has_offscreen_renderer\u7528\u4e8e\u83b7\u53d6\u56fe\u50cf，\u4e4b\u524dautodl\u4e0a\u8fdb\u884cmujoco\u5b9e\u8df5\u7528\u7684OffScreenRenderEnv\u5b9e\u8d28\u5c31\u662f\u7ee7\u627f\u4e8eControlEnv，\u4e14\u5176\u9ed8\u8ba4has_offscreen_renderer\u4e3atrue\u3002<\/p>\n

\u8f93\u51fa：<\/p>\n

(libero) dukiya@dukiya-XiaoXinPro-14-APH8:~\/libero-learning\/week4$ python -u A1_libero_keyboard_fps.py
\n[robosuite WARNING] No private macro file found! (__init__.py:7)
\n[robosuite WARNING] It is recommended to use a private macro file (__init__.py:8)
\n[robosuite WARNING] To setup, run: python \/home\/dukiya\/venvs\/libero\/lib\/python3.10\/site-packages\/robosuite\/scripts\/setup_macros.py (__init__.py:9)
\nUnable to load module hid, required to interface with SpaceMouse.<\/p>\n

Only macOS is officially supported. Install the additional<\/p>\n

requirements with `pip install -r requirements-extra.txt`
\n[info] using task orders [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
\n\u4efb\u52a1: pick up the black bowl between the plate and the ramekin and place it on the plate
\n\u6d4b\u8bd5\u6a21\u5f0f: \u4ec5 viewer
\nLocal assets not found. Downloading from HuggingFace Hub…
\nAssets already downloaded at \/home\/dukiya\/.cache\/libero\/assets
\nqt.qpa.plugin: Could not find the Qt platform plugin "wayland" in "\/home\/dukiya\/venvs\/libero\/lib\/python3.10\/site-packages\/cv2\/qt\/plugins"
\n\u5f00\u59cb\u6d4bfps, \u4e0d\u9700\u6309\u952e
\n\u7b2c 1 \u6b21: 100 \u6b65\u8017\u65f6=3.7127978919888847 \u79d2，FPS=26.933865755464446
\n\u7b2c 2 \u6b21: 100 \u6b65\u8017\u65f6=3.1702399159548804 \u79d2，FPS=31.543354020851723
\n\u7b2c 3 \u6b21: 100 \u6b65\u8017\u65f6=3.4133581139612943 \u79d2，FPS=29.296662307708257
\n\u6a21\u5f0f: \u4ec5 viewer
\n\u4e09\u6b21 FPS: [26.933865755464446, 31.543354020851723, 29.296662307708257]
\n\u5e73\u5747 FPS: 29.257960694674807<\/p>\n

Keys Command
\nq reset simulation
\nspacebar toggle gripper (open\/close)
\nw-a-s-d move arm horizontally in x-y plane
\nr-f move arm vertically
\nz-x rotate arm about x-axis
\nt-g rotate arm about y-axis
\nc-v rotate arm about z-axis
\nESC quit<\/p>\n

\u5f00\u59cb\u952e\u76d8\u64cd\u4f5c:
\nW\/A\/S\/D \u6c34\u5e73\u79fb\u52a8:R\/F \u5347\u964d；\u7a7a\u683c\u5f00\u5408\u5939\u722a\u3002
\n\u672c\u811a\u672c\u6309 Q \u7ed3\u675f，\u4e5f\u53ef\u4ee5\u5728\u7ec8\u7aef\u6309 Ctrl+C\u3002<\/p>\n

\"\"<\/p>\n

\u7ed3\u679c\u8868\u660e\u6211\u7684\u96c6\u6210\u663e\u5361\u9065\u64cd\u4f5c\u5b8c\u5168\u6ca1\u95ee\u9898，\u5e73\u5747\u5e27\u7387\u80fd\u8fbe\u523030\u5de6\u53f3\u3002<\/p>\n

\n

2. \u5728mujoco\u4e2d\u5f15\u5165piper<\/h3>\n

\u8fd9\u4e00\u6b65\u662f\u5728\u4e86\u89e3piper\u662f\u4ec0\u4e48，\u957f\u4ec0\u4e48\u6837\u3002<\/p>\n

2.1. piper\u662f\u4ec0\u4e48<\/h4>\n

Piper \u662f\u677e\u7075\u673a\u5668\u4eba（AgileX）\u63a8\u51fa\u7684\u4e00\u6b3e\u516d\u81ea\u7531\u5ea6\u673a\u68b0\u81c2，\u53ef\u4ee5\u63a7\u5236\u672b\u7aef\u5939\u722a\u5728\u7a7a\u95f4\u4e2d\u7684\u4f4d\u7f6e\u548c\u671d\u5411，\u5b8c\u6210\u6293\u53d6\u3001\u642c\u8fd0\u3001\u653e\u7f6e\u7b49\u64cd\u4f5c\u3002<\/p>\n

\u8fdb\u4e00\u6b65\u8bf4，piper\u662f\u56fd\u4ea7\u7684\u673a\u68b0\u81c2，pi05\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5e76\u6ca1\u6709\u5728\u5b83\u7684\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u8fc7，\u7528piper\u505a\u6b64\u5b9e\u8df5，\u4e00\u65b9\u9762\u662f\u66f4\u597d\u7684\u9002\u914d\u56fd\u5185\u591a\u6570\u4f01\u4e1a\u7684\u60c5\u51b5，\u4e00\u65b9\u9762\u80fd\u66f4\u597d\u7684\u4f53\u9a8cpi05\u5728\u672a\u89c1\u8fc7\u7684\u673a\u68b0\u81c2\u6a21\u578b\u4e0a\u7684\u9002\u914d\u60c5\u51b5\u3002<\/p>\n

2.2. \u4e0b\u8f7dpiper\u6a21\u578b\u5e76\u5f15\u5165mujoco<\/h4>\n

\u901a\u8fc7\u4e0b\u9762\u7684\u6307\u4ee4\u4e0b\u8f7dpiper\u6a21\u578b：<\/p>\n

source \/home\/dukiya\/venvs\/libero\/bin\/activate
\nunset PYTHONPATH
\nunset PYTHONHOME
\nunset MUJOCO_GL
\nunset PYOPENGL_PLATFORM<\/p>\n

mkdir -p \/home\/dukiya\/libero-learning\/week4\/assets
\ncd \/home\/dukiya\/libero-learning\/week4\/assets<\/p>\n

# \u53ea\u53d6 Piper \u5b50\u76ee\u5f55，\u907f\u514d\u4e0b\u8f7d\u6a21\u578b\u5e93\u4e2d\u7684\u5168\u90e8\u673a\u5668\u4eba
\ngit clone –depth 1 –filter=blob:none –sparse \\\\
\n https:\/\/github.com\/google-deepmind\/mujoco_menagerie.git<\/p>\n

git -C mujoco_menagerie sparse-checkout set agilex_piper<\/p>\n

# \u4fdd\u7559\u8fd9\u884c\u8f93\u51fa，\u4f5c\u4e3a\u5b9e\u9645\u4e0b\u8f7d\u7248\u672c\u7684\u8bb0\u5f55
\ngit -C mujoco_menagerie rev-parse HEAD<\/p>\n

\u7528\u4e4b\u524dmujoco\u5f15\u5165\u81ea\u5df1\u5199\u7684mjcf\u7684\u65b9\u6cd5，\u628a\u8def\u5f84\u627e\u5bf9，\u5c31\u80fd\u5728mujoco\u4e2d\u770b\u5230piper\u7684\u4eff\u771f：<\/p>\n

from pathlib import Path
\nimport mujoco
\nimport mujoco.viewer<\/p>\n

# scene.xml \u662f\u573a\u666f\u5165\u53e3，\u5185\u90e8\u5305\u542b\u673a\u5668\u4eba piper.xml
\nmodel_path = (
\n Path(__file__).resolve().parent
\n \/ "assets"
\n \/ "mujoco_menagerie"
\n \/ "agilex_piper"
\n \/ "scene.xml"
\n)<\/p>\n

model = mujoco.MjModel.from_xml_path(str(model_path))
\ndata = mujoco.MjData(model)<\/p>\n

# \u6309\u540d\u5b57\u627eid,\u4e2d\u95f4\u7684\u53c2\u6570\u8868\u660e\u67e5\u8be2\u5bf9\u8c61\u7684\u7c7b\u578b
\nhome_id = mujoco.mj_name2id(
\n model, mujoco.mjtObj.mjOBJ_KEY, "home"
\n)
\nassert home_id >= 0, "\u6a21\u578b\u4e2d\u6ca1\u6709\u627e\u5230 home \u59ff\u6001"<\/p>\n

mujoco.mj_resetDataKeyframe(model, data, home_id)
\nmujoco.mj_forward(model, data)<\/p>\n

print("=== Piper \u6a21\u578b\u4fe1\u606f ===")
\nprint("\u6a21\u578b\u8def\u5f84:", model_path)
\nprint("MuJoCo \u7248\u672c:", mujoco.__version__)
\nprint("\u5173\u8282\u6570 njnt:", model.njnt)
\nprint("\u4f4d\u7f6e\u72b6\u6001\u957f\u5ea6 nq:", model.nq)
\nprint("\u901f\u5ea6\u72b6\u6001\u957f\u5ea6 nv:", model.nv)
\nprint("\u6267\u884c\u5668\u6570 nu:", model.nu)
\nprint("\u7269\u7406\u6b65\u957f dt:", model.opt.timestep)<\/p>\n

print("\\\\n=== \u5173\u8282\u540d\u79f0\u4e0e\u9650\u4f4d ===")
\nfor index in range(model.njnt):
\n name = mujoco.mj_id2name(
\n model, mujoco.mjtObj.mjOBJ_JOINT, index
\n )
\n joint_type = model.jnt_type[index]<\/p>\n

if joint_type == mujoco.mjtJoint.mjJNT_HINGE:
\n description = "\u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad"
\n elif joint_type == mujoco.mjtJoint.mjJNT_SLIDE:
\n description = "\u6ed1\u52a8\u5173\u8282，\u5355\u4f4d m"
\n else:
\n description = f"\u5176\u4ed6\u5173\u8282\u7c7b\u578b {joint_type}"<\/p>\n

print(name, description, "\u9650\u4f4d", model.jnt_range[index])<\/p>\n

print("\\\\n=== \u6267\u884c\u5668\u4e0e\u521d\u59cb\u63a7\u5236\u503c ===")
\nfor index in range(model.nu):
\n name = mujoco.mj_id2name(
\n model, mujoco.mjtObj.mjOBJ_ACTUATOR, index
\n )
\n print(name, "\u521d\u59cb ctrl: ", data.ctrl[index])<\/p>\n

# \u6838\u5bf9\u5f53\u524d\u9009\u7528\u7684\u6a21\u578b\u7ed3\u6784
\nassert (model.njnt, model.nq, model.nu) == (8, 8, 7)<\/p>\n

# MuJoCo \u539f\u751f\u67e5\u770b\u5668\u8d1f\u8d23\u63a8\u8fdb\u7269\u7406\u4eff\u771f
\nmujoco.viewer.launch(model, data)<\/p>\n

\u8f93\u51fa：<\/p>\n

(libero) dukiya@dukiya-XiaoXinPro-14-APH8:~\/libero-learning\/week4$ python -u A2_piper_model.py
\n=== Piper \u6a21\u578b\u4fe1\u606f ===
\n\u6a21\u578b\u8def\u5f84: \/home\/dukiya\/libero-learning\/week4\/assets\/mujoco_menagerie\/agilex_piper\/scene.xml
\nMuJoCo \u7248\u672c: 3.3.7
\n\u5173\u8282\u6570 njnt: 8
\n\u4f4d\u7f6e\u72b6\u6001\u957f\u5ea6 nq: 8
\n\u901f\u5ea6\u72b6\u6001\u957f\u5ea6 nv: 8
\n\u6267\u884c\u5668\u6570 nu: 7
\n\u7269\u7406\u6b65\u957f dt: 0.002<\/p>\n

=== \u5173\u8282\u540d\u79f0\u4e0e\u9650\u4f4d ===
\njoint1 \u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad \u9650\u4f4d [-2.618 2.618]
\njoint2 \u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad \u9650\u4f4d [0. 3.14]
\njoint3 \u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad \u9650\u4f4d [-2.697 0. ]
\njoint4 \u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad \u9650\u4f4d [-1.832 1.832]
\njoint5 \u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad \u9650\u4f4d [-1.22 1.22]
\njoint6 \u8f6c\u52a8\u5173\u8282，\u5355\u4f4d rad \u9650\u4f4d [-3.14 3.14]
\njoint7 \u6ed1\u52a8\u5173\u8282，\u5355\u4f4d m \u9650\u4f4d [0. 0.035]
\njoint8 \u6ed1\u52a8\u5173\u8282，\u5355\u4f4d m \u9650\u4f4d [-0.035 0. ]<\/p>\n

=== \u6267\u884c\u5668\u4e0e\u521d\u59cb\u63a7\u5236\u503c ===
\njoint1 \u521d\u59cb ctrl: 0.0
\njoint2 \u521d\u59cb ctrl: 1.57
\njoint3 \u521d\u59cb ctrl: -1.3485
\njoint4 \u521d\u59cb ctrl: 0.0
\njoint5 \u521d\u59cb ctrl: 0.0
\njoint6 \u521d\u59cb ctrl: 0.0
\ngripper \u521d\u59cb ctrl: 0.0
\n\/home\/dukiya\/venvs\/libero\/lib\/python3.10\/site-packages\/glfw\/__init__.py:917: GLFWError: (65548) b'Wayland: The platform does not provide the window position'
\n warnings.warn(message, GLFWError)<\/p>\n

\"\"<\/p>\n

\u53ef\u4ee5\u770b\u5230，piper\u4e3a6\u4e2a\u5173\u8282\u52a01\u4e2a\u5939\u722a\u7ec4\u6210\u3002<\/p>\n

\n

3. \u5b9e\u73b0\u952e\u76d8\u9a71\u52a8piper<\/h3>\n

\u6211\u4eec\u540c\u6837\u662f\u8981\u57fa\u4e8erobosuite\u7684\u952e\u76d8\u63a5\u53e3\u505a\u8fd9\u4e2a\u9700\u6c42，\u7531\u4e8erobosuite\u5e76\u6ca1\u6709\u63d0\u524d\u9002\u914d\u597dpiper，\u56e0\u6b64\u6211\u4eec\u9700\u8981\u5199\u597d\u5bf9\u5e94\u7684\u9002\u914d\u63a5\u5165robosuite，\u5e76\u5b9e\u73b0\u5bf9\u5e94\u7684\u63a7\u5236\u903b\u8f91\u8ba9\u952e\u76d8\u63a7\u5236piper\u52a8\u8d77\u6765\u3002<\/p>\n

3.1. \u8f93\u51fa\u80fd\u88abrobosuite\u65f6\u7528\u7684\u673a\u68b0\u81c2xml\u548c\u5939\u722axml<\/h4>\n

\u8fd9\u4e00\u6b65\u5176\u5b9e\u53ea\u662f\u505a\u4e00\u4e9b\u683c\u5f0f\u7684\u8f6c\u6362\u548c\u6807\u7b7e\u8865\u9f50\u3002\u539f\u59cb\u7684piper\u6a21\u578b\u80fd\u591f\u88abmujoco\u8bfb\u53d6，\u4f46\u8fd8\u4e0d\u6ee1\u8db3robosuite\u8bfb\u53d6，\u6211\u4eec\u53ea\u9700\u8981\u5efa\u7acb\u811a\u672c\u8fdb\u884c\u8f6c\u6362\u5c31\u884c：<\/p>\n

from copy import deepcopy
\nfrom pathlib import Path
\nimport xml.etree.ElementTree as ET<\/p>\n

from robosuite.models.base import MujocoXML<\/p>\n

project = Path(__file__).resolve().parent
\nsource_dir = project \/ "assets\/mujoco_menagerie\/agilex_piper"
\noutput_dir = project \/ "assets\/piper_robosuite"
\noutput_dir.mkdir(parents=True, exist_ok=True)<\/p>\n

source = ET.parse(source_dir \/ "piper.xml").getroot()<\/p>\n

# 1. \u5c55\u5f00\u5d4c\u5957 default \u548c childclass
\n# robosuite 1.4.0 \u7684\u8bfb\u53d6\u5668\u4e0d\u80fd\u76f4\u63a5\u5904\u7406\u539f\u6a21\u578b\u7684\u8fd9\u5957\u5d4c\u5957\u914d\u7f6e
\ndefaults = {}<\/p>\n

def collect_defaults(node, inherited):
\n settings = deepcopy(inherited)<\/p>\n

for child in node:
\n if child.tag != "default":
\n settings.setdefault(child.tag, {}).update(child.attrib)<\/p>\n

defaults[node.get("class", "main")] = settings<\/p>\n

for child in node.findall("default"):
\n collect_defaults(child, settings)<\/p>\n

def expand_defaults(node, inherited_class="main"):
\n class_name = node.attrib.pop("class", inherited_class)<\/p>\n

for key, value in defaults[class_name].get(node.tag, {}).items():
\n node.attrib.setdefault(key, value)<\/p>\n

child_class = node.attrib.pop("childclass", inherited_class)<\/p>\n

for child in node:
\n expand_defaults(child, child_class)<\/p>\n

default_node = source.find("default")
\nassert default_node is not None<\/p>\n

collect_defaults(default_node, {})
\nsource.remove(default_node)
\nexpand_defaults(source)<\/p>\n

# 2. \u660e\u786e\u7f51\u683c\u540d\u79f0\u548c\u6587\u4ef6\u8def\u5f84
\n# \u539f\u6a21\u578b\u6709\u4e9b mesh \u6ca1\u5199 name，MuJoCo \u4f1a\u81ea\u52a8\u547d\u540d，
\n# \u4f46 robosuite \u6dfb\u52a0\u673a\u5668\u4eba\u524d\u7f00\u65f6\u9700\u8981\u660e\u786e\u7684\u540d\u79f0
\nsource.find("compiler").attrib.pop("meshdir", None)<\/p>\n

for mesh in source.findall("asset\/mesh"):
\n original_file = mesh.get("file")
\n mesh.attrib.setdefault("name", Path(original_file).stem)
\n mesh.set(
\n "file",
\n str((source_dir \/ "assets" \/ original_file).resolve()),
\n )<\/p>\n

# 3. \u8865\u51e0\u4f55\u4f53\u540d\u79f0，\u5e76\u6362\u6210 robosuite \u7684\u663e\u793a\u5206\u7ec4
\n# group=0：\u78b0\u649e\u51e0\u4f55；group=1：\u89c6\u89c9\u51e0\u4f55
\nfor body in source.iter("body"):
\n body.attrib.pop("gravcomp", None)<\/p>\n

for index, geom in enumerate(body.findall("geom")):
\n geom.attrib.setdefault(
\n "name", f"{body.get('name')}_geom_{index}"
\n )
\n is_visual = (
\n geom.get("contype", "1") == "0"
\n and geom.get("conaffinity", "1") == "0"
\n )
\n geom.set("group", "1" if is_visual else "0")<\/p>\n

arm = deepcopy(source)
\ngripper = deepcopy(source)<\/p>\n

# 4. \u673a\u68b0\u81c2：\u79fb\u9664\u4e24\u6839\u624b\u6307，\u6dfb\u52a0\u5939\u722a\u5b89\u88c5\u4f4d\u7f6e
\nwrist = arm.find(".\/\/body[@name='link6']")
\nassert wrist is not None<\/p>\n

for name in ["link7", "link8"]:
\n finger = wrist.find(f"body[@name='{name}']")
\n assert finger is not None
\n wrist.remove(finger)<\/p>\n

hand = ET.SubElement(
\n wrist, "body", name="right_hand", pos="0 0 0"
\n)<\/p>\n

# \u8155\u90e8\u76f8\u673a\u5148\u7ed9\u4e00\u4e2a\u521d\u59cb\u914d\u7f6e，\u8fdb\u5165\u573a\u666f\u540e\u518d\u8c03\u6574\u53d6\u666f
\nET.SubElement(
\n hand,
\n "camera",
\n name="eye_in_hand",
\n pos="0.04 0 0.025",
\n quat="0 1 0 0",
\n fovy="80",
\n)<\/p>\n

# \u539f\u6765\u7684\u706f\u6307\u5411 link8；\u624b\u6307\u62c6\u8d70\u540e\u4e0d\u518d\u4fdd\u7559\u8be5\u706f
\nworld = arm.find("worldbody")
\nfor light in list(world.findall("light")):
\n world.remove(light)<\/p>\n

for tag in ["equality", "keyframe"]:
\n node = arm.find(tag)
\n if node is not None:
\n arm.remove(node)<\/p>\n

# \u516d\u4e2a\u673a\u68b0\u81c2\u6267\u884c\u5668\u6539\u4e3a\u529b\u77e9\u63a7\u5236
\nactuators = arm.find("actuator")
\nactuators.clear()<\/p>\n

for index in range(1, 7):
\n ET.SubElement(
\n actuators,
\n "motor",
\n name=f"joint{index}",
\n joint=f"joint{index}",
\n gear="1",
\n ctrllimited="true",
\n ctrlrange="-100 100",
\n forcelimited="true",
\n forcerange="-100 100",
\n )<\/p>\n

# 5. \u5939\u722a：\u4fdd\u7559\u539f\u6765\u7684\u4e24\u6839\u624b\u6307\u4e0e\u8054\u52a8\u7ea6\u675f
\noriginal_wrist = gripper.find(".\/\/body[@name='link6']")
\nfingers = [
\n deepcopy(original_wrist.find(f"body[@name='{name}']"))
\n for name in ["link7", "link8"]
\n]<\/p>\n

gripper_world = gripper.find("worldbody")
\ngripper_world.clear()
\ngripper_body = ET.SubElement(
\n gripper_world, "body", name="right_gripper", pos="0 0 0"
\n)<\/p>\n

for finger, side in zip(fingers, ["left", "right"]):
\n boxes = [
\n geom for geom in finger.findall("geom")
\n if geom.get("type") == "box"
\n ]
\n assert len(boxes) == 2<\/p>\n

boxes[0].set("name", f"{side}_finger_collision")
\n boxes[1].set("name", f"{side}_fingerpad_collision")
\n gripper_body.append(finger)<\/p>\n

for tag in ["contact", "keyframe"]:
\n node = gripper.find(tag)
\n if node is not None:
\n gripper.remove(node)<\/p>\n

gripper_actuators = gripper.find("actuator")
\nposition_actuator = deepcopy(
\n gripper_actuators.find("position[@name='gripper']")
\n)
\nassert position_actuator is not None<\/p>\n

gripper_actuators.clear()
\nposition_actuator.attrib.pop("inheritrange", None)
\nposition_actuator.set("ctrllimited", "true")
\nposition_actuator.set("ctrlrange", "0 0.035")
\ngripper_actuators.append(position_actuator)<\/p>\n

# 6. \u6dfb\u52a0 robosuite \u9700\u8981\u7684\u672b\u7aef\u6807\u8bb0\u548c\u529b\/\u529b\u77e9\u4f20\u611f\u5668
\n# 0.09003 = \u624b\u6307\u5b89\u88c5\u4f4d\u7f6e 0.13503 – \u8fdc\u7aef\u5939\u6301\u5757\u504f\u79fb 0.045
\neef = ET.SubElement(
\n gripper_body, "body", name="eef", pos="0 0 0.09003"
\n)<\/p>\n

for name, position in [
\n ("grip_site", "0 0 0"),
\n ("ee", "0 0 0"),
\n ("ee_x", "0.03 0 0"),
\n ("ee_y", "0 0.03 0"),
\n ("ee_z", "0 0 0.03"),
\n]:
\n ET.SubElement(
\n eef, "site", name=name, pos=position,
\n type="sphere", size="0.004", group="1",
\n )<\/p>\n

ET.SubElement(
\n eef, "site", name="grip_site_cylinder",
\n type="cylinder", size="0.003 0.03", group="1",
\n)<\/p>\n

ET.SubElement(
\n gripper_body, "site", name="ft_frame",
\n pos="0 0 0", size="0.004", group="1",
\n)<\/p>\n

sensors = gripper.find("sensor")
\nif sensors is None:
\n sensors = ET.SubElement(gripper, "sensor")<\/p>\n

ET.SubElement(sensors, "force", name="force_ee", site="ft_frame")
\nET.SubElement(sensors, "torque", name="torque_ee", site="ft_frame")<\/p>\n

# 7. \u5220\u9664\u5404\u90e8\u5206\u4e0d\u518d\u4f7f\u7528\u7684\u7f51\u683c\u3001\u6750\u8d28
\ndef trim_assets(root):
\n meshes = {
\n geom.get("mesh") for geom in root.iter("geom")
\n if geom.get("mesh") is not None
\n }
\n materials = {
\n geom.get("material") for geom in root.iter("geom")
\n if geom.get("material") is not None
\n }<\/p>\n

assets = root.find("asset")
\n for item in list(assets):
\n unused_mesh = (
\n item.tag == "mesh" and item.get("name") not in meshes
\n )
\n unused_material = (
\n item.tag == "material"
\n and item.get("name") not in materials
\n )
\n if unused_mesh or unused_material:
\n assets.remove(item)<\/p>\n

trim_assets(arm)
\ntrim_assets(gripper)<\/p>\n

# \u5df2\u6709\u6587\u4ef6\u5982\u679c\u5185\u5bb9\u4e0d\u540c，\u5219\u505c\u6b62，\u907f\u514d\u8986\u76d6\u540e\u7eed\u624b\u52a8\u4fee\u6539
\ndef save_text(path, text):
\n if path.exists():
\n if path.read_text(encoding="utf-8") != text:
\n raise FileExistsError(
\n f"{path} \u5df2\u5b58\u5728\u4e14\u5185\u5bb9\u4e0d\u540c，\u8bf7\u5148\u5907\u4efd\u518d\u91cd\u65b0\u751f\u6210"
\n )
\n else:
\n path.write_text(text, encoding="utf-8")<\/p>\n

for root, filename in [
\n (arm, "piper_arm.xml"),
\n (gripper, "piper_gripper.xml"),
\n]:
\n ET.indent(root, space=" ")
\n save_text(
\n output_dir \/ filename,
\n ET.tostring(root, encoding="unicode"),
\n )<\/p>\n

save_text(
\n output_dir \/ "LICENSE",
\n (source_dir \/ "LICENSE").read_text(encoding="utf-8"),
\n)<\/p>\n

# 8. \u7528 robosuite \u8bfb\u53d6，\u518d\u7531 MuJoCo \u7f16\u8bd1，\u68c0\u67e5\u62c6\u5206\u548c\u5408\u5e76\u7ed3\u6784
\narm_xml = MujocoXML(str(output_dir \/ "piper_arm.xml"))
\ngripper_xml = MujocoXML(str(output_dir \/ "piper_gripper.xml"))<\/p>\n

arm_model = arm_xml.get_model()
\ngripper_model = gripper_xml.get_model()<\/p>\n

print("\u673a\u68b0\u81c2 nq \/ nu:", arm_model.nq, arm_model.nu)
\nprint("\u5939\u722a nq \/ nu:", gripper_model.nq, gripper_model.nu)<\/p>\n

assert (arm_model.nq, arm_model.nu) == (6, 6)
\nassert (gripper_model.nq, gripper_model.nu) == (2, 1)<\/p>\n

arm_xml.merge(gripper_xml, merge_body="right_hand")
\ncombined = arm_xml.get_model()<\/p>\n

print("\u5408\u5e76\u540e nq \/ nu:", combined.nq, combined.nu)
\nassert (combined.nq, combined.nu) == (8, 7)<\/p>\n

print("\u9002\u914d\u6587\u4ef6\u76ee\u5f55:", output_dir)
\nprint("A3_PIPER_XML_OK")<\/p>\n

\u8f93\u51fa：<\/p>\n

(libero) dukiya@dukiya-XiaoXinPro-14-APH8:~\/libero-learning\/week4$ \/home\/dukiya\/venvs\/libero\/bin\/python \/home\/dukiya\/libero-learning\/week4\/A3_1_prepare_piper.py
\n[robosuite WARNING] No private macro file found! (__init__.py:7)
\n[robosuite WARNING] It is recommended to use a private macro file (__init__.py:8)
\n[robosuite WARNING] To setup, run: python \/home\/dukiya\/venvs\/libero\/lib\/python3.10\/site-packages\/robosuite\/scripts\/setup_macros.py (__init__.py:9)
\n\u673a\u68b0\u81c2 nq \/ nu: 6 6
\n\u5939\u722a nq \/ nu: 2 1
\n\u5408\u5e76\u540e nq \/ nu: 8 7
\n\u9002\u914d\u6587\u4ef6\u76ee\u5f55: \/home\/dukiya\/libero-learning\/week4\/assets\/piper_robosuite
\nA3_PIPER_XML_OK<\/p>\n

\u6838\u5fc3\u6539\u53d8\u5982\u4e0b：<\/p>\n

\"\"<\/p>\n

3.2. \u6ce8\u518cpiper<\/h4>\n

\u5f97\u5230\u4e86\u80fd\u88abrobosuite\u8bc6\u522b\u7684xml，\u6211\u4eec\u4e0b\u4e00\u6b65\u5c31\u9700\u8981\u8fdb\u884c\u76f8\u5e94\u7684\u6ce8\u518c，\u8ba9\u6700\u540e\u4e66\u5199\u53c2\u6570robots=["Piper"]\u80fd\u6210\u529f\u88ab\u7cfb\u7edf\u8bc6\u522b\u3002<\/p>\n

\u6ce8\u518c\u7c7b：<\/p>\n

from pathlib import Path
\nimport numpy as np
\nfrom robosuite.models.robots.manipulators.manipulator_model import ManipulatorModel
\nfrom robosuite.models.grippers.gripper_model import GripperModel
\nfrom robosuite.models.grippers import GRIPPER_MAPPING
\nfrom robosuite.robots import ROBOT_CLASS_MAPPING, SingleArm<\/p>\n

# \u5728robosuite\u4e2d\u6ce8\u518cpiper\u8fd9\u4e2a\u5168\u65b0\u7684\u673a\u68b0\u81c2
\nMODEL_DIR = (
\n Path(__file__).resolve().parent
\n \/ "assets"
\n \/ "piper_robosuite"
\n)<\/p>\n

class PiperGripper(GripperModel):
\n def __init__(self, idn=0):
\n super().__init__(
\n str(MODEL_DIR \/ "piper_gripper.xml"),
\n idn=idn,
\n )<\/p>\n

@property
\n # @property\u53ef\u4ee5\u8ba9\u4e00\u4e2a\u65b9\u6cd5\u4f2a\u88c5\u6210\u5c5e\u6027\u6765\u4f7f\u7528，\u8bbf\u95ee\u65f6\u4e0d\u7528\u518d\u52a0\u62ec\u53f7
\n def init_qpos(self):
\n # \u4e24\u6839\u624b\u6307\u7684\u4f4d\u7f6e：\u521d\u59cb\u5b8c\u5168\u5f20\u5f00
\n return np.array([0.035, -0.035])<\/p>\n

def format_action(self, action):
\n action = np.asarray(action, dtype=float)
\n assert action.shape == (1, )
\n # robosuite\u4e0b，\u7ed9\u5939\u722a-1\u8868\u793a\u5f20\u5f00，+1\u8868\u793a\u95ed\u5408
\n # \u5bf9piper xml\u6765\u8bb2，\u63a7\u5236\u503c\u8d8a\u5927，\u5939\u722a\u5f20\u7684\u8d8a\u5f00
\n # \u56e0\u6b64\u53cd\u8f6c\u7b26\u53f7, \u6846\u67b6\u968f\u540e\u4f1a\u6620\u5c04\u5230 [0, 0.035] \u7c73
\n # clip\u7528\u4e8e\u6536\u7d27\u8303\u56f4，\u4e0d\u7ba1\u53d6\u503c\u4e3a\u591a\u5c11，\u6700\u7ec8\u7684\u8303\u56f4\u53ea\u80fd\u5728-1，+1\u4e4b\u95f4
\n self.current_action = -np.clip(action, -1.0, 1.0)
\n return self.current_action<\/p>\n

@property
\n def _important_geoms(self):
\n # \u6307\u5b9a\u54ea\u4e9b\u78b0\u649e\u4f53\u662f\u624b\u6307\u3001\u54ea\u4e9b\u662f\u5939\u6301\u9762
\n return {
\n "left_finger":[
\n "left_finger_collision",
\n "left_fingerpad_collision",
\n ],
\n "right_finger":[
\n "right_finger_collision",
\n "right_fingerpad_collision",
\n ],
\n "left_fingerpad":["left_fingerpad_collision"],
\n "right_fingerpad":["right_fingerpad_collision"],
\n }<\/p>\n

class Piper(ManipulatorModel):
\n def __init__(self, idn=0):
\n super().__init__(
\n str(MODEL_DIR \/ "piper_arm.xml"),
\n idn=idn,
\n )<\/p>\n

# \u8155\u90e8\u76f8\u673a：\u5411\u4fa7\u9762\u79fb\u5f00，\u5e76\u671d\u5411\u5939\u722a\u524d\u65b9
\n camera_name = self.correct_naming("eye_in_hand")
\n camera = self.worldbody.find(
\n f".\/\/camera[@name='{camera_name}']"
\n )
\n assert camera is not None<\/p>\n

# \u8bbe\u7f6e\u624b\u8155\u76f8\u673a\u4f4d\u7f6e
\n camera.set("pos", "0.06 0 0.04")
\n camera.set("quat", "0 0.97324898946773 0 0.22975292054736")<\/p>\n

# \u7ed9\u673a\u68b0\u81c2\u76846\u4e2a\u5173\u8282\u8bbe\u7f6e\u7edf\u4e000.1\u7684\u963b\u5c3c\u7cfb\u6570
\n self.set_joint_attribute(
\n attrib="damping",
\n values=np.full(6, 0.1),
\n )<\/p>\n

@property
\n def default_mount(self):
\n # \u76f4\u63a5\u56fa\u5b9a\u5728\u684c\u9762，\u4e0d\u4f7f\u7528 Panda \u7684\u652f\u6491\u67b6
\n return None<\/p>\n

@property
\n def default_gripper(self):
\n return "PiperGripper"<\/p>\n

@property
\n def default_controller_config(self):
\n return "osc_pose"<\/p>\n

@property
\n def init_qpos(self):
\n # \u57fa\u4e8e\u539f\u6a21\u578b home \u59ff\u6001，\u7a0d\u5fae\u5f2f\u66f2\u7b2c\u4e94\u5173\u8282
\n # \u907f\u514d\u521d\u59cb\u8155\u90e8\u5b8c\u5168\u4f38\u76f4；\u4e0d\u662f\u6700\u7ec8\u6293\u53d6\u59ff\u6001
\n return np.array([0.0, 1.57, -1.3485, 0.0, 0.35, 0.0])<\/p>\n

# \u4e0b\u9762\u56db\u4e2a\u5c5e\u6027\u8ba9robosuite\u77e5\u9053\u4e86\u673a\u68b0\u81c2\u7684\u5305\u56f4\u8303\u56f4
\n @property
\n # \u51b3\u5b9a\u673a\u68b0\u81c2\u653e\u5728\u54ea
\n def base_xpos_offset(self):
\n return {
\n # \u5f53\u524d\u9ed1\u7897\u573a\u666f\u684c\u9762\u9ad8 0.90 \u7c73
\n # \u5e95\u5ea7\u653e\u5728\u684c\u9762\u7684\u8d1f x \u8fb9\u7f18\u9644\u8fd1，\u671d\u5411\u6b63 x
\n "table": lambda table_length: (
\n -table_length \/ 2 + 0.05,
\n 0.0,
\n 0.90,
\n ),
\n }<\/p>\n

@property
\n # \u673a\u68b0\u81c2\u6700\u9ad8\u70b9\u5927\u7ea6\u5728\u5e95\u5ea7\u4e0a\u65b9 0.9 \u7c73\u5904
\n def top_offset(self):
\n return np.array([0.0, 0.0, 0.9])<\/p>\n

@property
\n # \u673a\u68b0\u81c2\u6c34\u5e73\u65b9\u5411\u534a\u5f84
\n def _horizontal_radius(self):
\n return 0.9<\/p>\n

@property
\n def horizontal_radius(self):
\n # \u6ca1\u6709 mount \u65f6，\u907f\u514d\u7236\u7c7b\u8bbf\u95ee mount.horizontal_radius
\n return self._horizontal_radius<\/p>\n

@property
\n def arm_type(self):
\n return "single"<\/p>\n

class MountedPiper(Piper):
\n pass<\/p>\n

# \u52a0\u5165robosuite\u5939\u722a\u6ce8\u518c\u8868
\nGRIPPER_MAPPING["PiperGripper"] = PiperGripper
\n# \u52a0\u5165\u673a\u68b0\u81c2\u6ce8\u518c\u8868，SingleArm\u4ee3\u8868\u8fd0\u884c\u65f6\u7c7b
\nROBOT_CLASS_MAPPING.update({
\n "Piper": SingleArm,
\n "MountedPiper": SingleArm,
\n})<\/p>\n

\u7ee7\u627frobotsuite\u7684\u76f8\u5173\u7c7b，\u628a\u4fe1\u606f\u6539\u6210piper\u5c31\u5b9e\u73b0\u4e86\u6ce8\u518c\u3002<\/p>\n

3.3. \u63a5\u5165\u952e\u76d8\u63a7\u5236<\/h4>\n

\u7528\u5982\u4e0b\u4ee3\u7801\u5b9e\u73b0\u952e\u76d8\u63a7\u5236piper：<\/p>\n

import time
\nfrom pathlib import Path
\nimport cv2
\nfrom robosuite import macros<\/p>\n

import numpy as np
\nimport piper_robot<\/p>\n

from libero.libero import benchmark, get_libero_path
\nfrom libero.libero.envs.env_wrapper import ControlEnv
\n# \u7528robosuite\u5b9a\u4e49\u952e\u76d8\u8f93\u5165\u8bbe\u5907\u548c\u52a8\u4f5c\u6620\u5c04
\nfrom robosuite.devices import Keyboard
\nfrom robosuite.utils.input_utils import input2action
\nfrom robosuite.utils import transform_utils as T<\/p>\n

# \u952e\u76d8\u589e\u91cf\u5230\u76ee\u6807\u4f4d\u59ff\u7684\u7f29\u653e；\u53ef\u4ee5\u5728\u624b\u611f\u6d4b\u8bd5\u540e\u8c03\u6574
\nPOSITION_GAIN = 0.01
\nROTATION_GAIN = 0.5
\n# \u9650\u5236\u76ee\u6807\u9886\u5148\u5b9e\u9645\u672b\u7aef\u7684\u8ddd\u79bb\u548c\u89d2\u5ea6，\u907f\u514d\u79ef\u7d2f\u4e0d\u53ef\u8fbe\u76ee\u6807
\nMAX_POSITION_LEAD = 0.03
\nMAX_ROTATION_LEAD = 0.2<\/p>\n

suite = benchmark.get_benchmark_dict()["libero_spatial"]()
\ntask = suite.get_task(0)<\/p>\n

bddl_path = (
\n Path(get_libero_path("bddl_files")) # \u7b2c 1 \u90e8\u5206：\u6839\u76ee\u5f55
\n \/ task.problem_folder # \u7b2c 2 \u90e8\u5206：\u4efb\u52a1\u5b50\u76ee\u5f55
\n \/ task.bddl_file # \u7b2c 3 \u90e8\u5206：BDDL \u6587\u4ef6\u540d
\n)<\/p>\n

print(f"\u4efb\u52a1: {task.language}")
\n# \u673a\u68b0\u81c2\u7528piper
\nenv = ControlEnv(
\n bddl_file_name=str(bddl_path),
\n robots=["Piper"],
\n controller="OSC_POSE",
\n initialization_noise=None,
\n control_freq=20,
\n # \u6253\u5f00\u4e3b\u7a97\u53e3
\n has_renderer=True,
\n has_offscreen_renderer=True,
\n render_camera="agentview",
\n # \u662f\u5426\u5728\u89c2\u6d4bobs\u91cc\u8fd4\u56de\u56fe\u50cf，True\u7684\u8bddobs\u5c31\u6709agentview_image\u3001robot0_eye_in_hand_image\u56fe\u50cf
\n use_camera_obs=True,
\n camera_names=["agentview", "robot0_eye_in_hand"],
\n camera_widths=256,
\n camera_heights=256,
\n camera_depths=False,
\n ignore_done=True,
\n)<\/p>\n

device = None<\/p>\n

try:
\n env.seed(1000)
\n observation = env.reset()<\/p>\n

robot = env.robots[0]
\n controller = robot.controller
\n controller.update(force=True)
\n target_pos = controller.ee_pos.copy()
\n target_ori = controller.ee_ori_mat.copy()
\n # \u6309\u952e\u66f4\u65b0\u76ee\u6807；\u677e\u5f00\u540e\u7ee7\u7eed\u5b88\u4f4f\u540c\u4e00\u4e2a\u76ee\u6807，\u4e0d\u8ffd\u968f\u5b9e\u9645\u4f4d\u7f6e\u6f02\u79fb
\n controller.uncoupling = False
\n controller.use_delta = False
\n print("\\\\n=== Piper \u63a5\u5165\u4fe1\u606f ===")
\n print("\u673a\u5668\u4eba\u6a21\u578b:", type(robot.robot_model).__name__)
\n print("\u673a\u68b0\u81c2\u5173\u8282\u6570:", robot.robot_model.dof)
\n print("\u5939\u722a:", type(robot.gripper).__name__)
\n print("\u52a8\u4f5c\u7ef4\u5ea6:", robot.action_dim)
\n print("\u521d\u59cb\u5173\u8282\u89d2:", observation["robot0_joint_pos"])
\n print("\u672b\u7aef\u4f4d\u7f6e:", observation["robot0_eef_pos"])
\n print("\u5939\u722a\u4f4d\u7f6e:", observation["robot0_gripper_qpos"])<\/p>\n

assert robot.robot_model.dof == 6
\n assert robot.action_dim == 7<\/p>\n

print("\\\\n=== \u63a7\u5236\u5668 ===")
\n print("\u7c7b\u578b:", controller.name)
\n print("\u76f8\u5bf9\u63a7\u5236:", controller.use_delta)
\n print("\u4f4d\u7f6e\/\u671d\u5411\u5206\u5f00\u8ba1\u7b97:", controller.uncoupling)
\n print("kp:", controller.kp)
\n print("kd(\u963b\u5c3c\u589e\u76ca):", controller.kd)
\n print("damping_ratio(\u7531 kp\u3001kd \u8fd8\u539f):",
\n controller.kd \/ (2 * np.sqrt(controller.kp)))
\n print("\u76f8\u5bf9\u63a7\u5236\u7684\u9ed8\u8ba4\u7f29\u653e\u4e0a\u9650（\u5f53\u524d\u7edd\u5bf9\u6a21\u5f0f\u4e0d\u4f7f\u7528）:", controller.output_max)
\n print("\u952e\u76d8\u4f4d\u7f6e\/\u65cb\u8f6c\u589e\u76ca:", POSITION_GAIN, ROTATION_GAIN)
\n print("\u76ee\u6807\u9886\u5148\u4e0a\u9650 \/ m\u3001rad:", MAX_POSITION_LEAD, MAX_ROTATION_LEAD)
\n print("\u63a7\u5236\u9891\u7387:", env.env.control_freq)
\n print("\u7269\u7406\u6b65\u957f:", env.sim.model.opt.timestep)<\/p>\n

root_body = robot.robot_model.root_body
\n print("\\\\n=== \u5e95\u5ea7 ===")
\n print("\u4e16\u754c\u4f4d\u7f6e:", env.sim.data.get_body_xpos(root_body))
\n print("\u4e16\u754c\u56db\u5143\u6570 wxyz:", env.sim.data.get_body_xquat(root_body))<\/p>\n

print("\\\\n=== \u76f8\u673a ===")
\n for name in ["agentview", "robot0_eye_in_hand"]:
\n camera_id = env.sim.model.camera_name2id(name)
\n key = f"{name}_image"<\/p>\n

print(name)
\n print(" \u56fe\u50cf\u5f62\u72b6:", observation[key].shape)
\n print(" \u5c40\u90e8\u4f4d\u7f6e:", env.sim.model.cam_pos[camera_id])
\n print(" \u5c40\u90e8\u56db\u5143\u6570:", env.sim.model.cam_quat[camera_id])
\n print(" fovy:", env.sim.model.cam_fovy[camera_id])<\/p>\n

assert observation[key].shape == (256, 256, 3)<\/p>\n

# \u5148\u4fdd\u6301\u4f4d\u7f6e\u5e76\u5f20\u5f00\u5939\u722a，\u89c2\u5bdf\u662f\u5426\u7a33\u5b9a
\n # \u7edd\u5bf9\u6a21\u5f0f\u4e0d\u80fd\u7528\u5168\u96f6\u4f4d\u59ff：\u4fdd\u6301\u521d\u59cb\u5316\u65f6\u7684\u4f4d\u7f6e\u548c\u671d\u5411
\n idle_action = np.concatenate([
\n target_pos,
\n T.quat2axisangle(T.mat2quat(target_ori)),
\n [-1.0],
\n ])
\n initial_eef = observation["robot0_eef_pos"].copy()<\/p>\n

for _ in range(20):
\n observation, _, _, _ = env.step(idle_action)
\n env.env.render()
\n assert np.isfinite(env.sim.data.qpos).all()<\/p>\n

print("\\\\n\u4fdd\u6301 20 \u6b65\u540e\u7684\u672b\u7aef\u4f4d\u79fb:")
\n print(observation["robot0_eef_pos"] – initial_eef)
\n print("A3_PIPER_ENV_READY")<\/p>\n

device = Keyboard(
\n pos_sensitivity=0.1,
\n rot_sensitivity=0.2,
\n )
\n device.start_control()<\/p>\n

print("\\\\n\u5f00\u59cb\u952e\u76d8\u6d4b\u8bd5:")
\n print("W\/A\/S\/D \u5e73\u79fb；R\/F \u5347\u964d；\u7a7a\u683c\u5f00\u5408\u5939\u722a\u3002")
\n print("Z\/X\u3001T\/G\u3001C\/V \u65cb\u8f6c；Q \u7ed3\u675f\u3002")
\n print("\u63a7\u5236\u65b9\u5f0f：\u6309\u952e\u66f4\u65b0\u56fa\u5b9a\u76ee\u6807，\u677e\u5f00\u540e\u4fdd\u6301\u76ee\u6807\u3002")
\n print("\u82e5\u660e\u663e\u6296\u52a8\u3001\u5931\u63a7\u6216\u7a7f\u684c，\u7acb\u5373 Ctrl+C，\u5e76\u4fdd\u7559\u8f93\u51fa\u3002")<\/p>\n

joint_names = robot.robot_model.joints
\n joint_ids = [
\n env.sim.model.joint_name2id(name)
\n for name in joint_names
\n ]<\/p>\n

print("\u5173\u8282\u987a\u5e8f:", joint_names)
\n print("\u5173\u8282\u9650\u4f4d \/ rad:\\\\n", env.sim.model.jnt_range[joint_ids])
\n last_log = time.perf_counter()<\/p>\n

while True:
\n start = time.perf_counter()<\/p>\n

action, grasp = input2action(
\n device=device,
\n robot=robot,
\n active_arm="right",
\n )<\/p>\n

if action is None:
\n break<\/p>\n

assert action.shape == (7,)
\n assert np.isfinite(action).all()
\n controller.update(force=True)<\/p>\n

candidate_pos = target_pos + action[:3] * POSITION_GAIN
\n if np.linalg.norm(candidate_pos – controller.ee_pos) <= MAX_POSITION_LEAD:
\n target_pos = candidate_pos<\/p>\n

delta_rotation = T.quat2mat(
\n T.axisangle2quat(action[3:6] * ROTATION_GAIN)
\n )
\n # \u540c\u6837，\u8f6c\u592a\u731b\u4e86\u5c31\u820d\u5f03，\u8fd9\u91cc\u6700\u597d\u63a2\u7a76\u6e05\u695a\u7ebf\u6027\u4ee3\u6570\u5173\u7cfb
\n # \u4e3a\u5565\u4e0d\u76f4\u63a5\u7528delta_rotation\u7b97rotation_gap？\u56e0\u4e3a\u5faa\u73af\u8dd1\u8d77\u6765\u4e4b\u540etarget_ori\u8ddfcontroller.ee_ori_mat\u4e0d\u4e00\u5b9a\u76f8\u7b49
\n # \u4e0a\u9762\u662f\u539f\u6ce8\u91ca；\u73b0\u5728\u8d85\u9650\u53ea\u622a\u65ad\u591a\u4f59\u89d2\u5ea6，\u4e0d\u518d\u6574\u6b65\u820d\u5f03
\n candidate_ori = delta_rotation @ target_ori
\n rotation_gap = T.quat2axisangle(
\n T.mat2quat(candidate_ori @ controller.ee_ori_mat.T)
\n )
\n if np.linalg.norm(rotation_gap) <= MAX_ROTATION_LEAD:
\n target_ori = candidate_ori<\/p>\n

# action \u662f\u952e\u76d8\u589e\u91cf；command \u662f\u53d1\u7ed9 OSC \u7684\u7edd\u5bf9\u76ee\u6807\u4f4d\u59ff
\n command = np.concatenate([
\n target_pos,
\n T.quat2axisangle(T.mat2quat(target_ori)),
\n action[6:7],
\n ])
\n observation, _, _, _ = env.step(command)
\n env.env.render()<\/p>\n

for key in ["agentview_image", "robot0_eye_in_hand_image"]:
\n frame = observation[key]
\n assert frame.shape == (256, 256, 3)<\/p>\n

# \u4ec5\u8f6c\u6362\u663e\u793a\u65b9\u5411\u548c\u989c\u8272，\u4e0d\u4fee\u6539\u539f\u59cb\u89c2\u6d4b
\n if macros.IMAGE_CONVENTION == "opengl":
\n frame = frame[::-1]<\/p>\n

# \u53ea\u5bf9\u5939\u722a\u76f8\u673a\u989d\u5916\u9006\u65f6\u9488\u65cb\u8f6c 90 \u5ea6
\n if key == "robot0_eye_in_hand_image":
\n frame = np.rot90(frame, k=1, axes=(0, 1))
\n frame = np.ascontiguousarray(frame)<\/p>\n

cv2.imshow(
\n key,
\n cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
\n )<\/p>\n

cv2.waitKey(1)
\n # \u6bcf\u79d2\u6253\u5370\u4e00\u6b21，\u907f\u514d\u5237\u5c4f\u5f71\u54cd\u64cd\u4f5c
\n if time.perf_counter() – last_log >= 1.0:
\n angles = np.array([
\n env.sim.data.get_joint_qpos(name)
\n for name in joint_names
\n ])<\/p>\n

print("\u5b9e\u9645\u5173\u8282\u89d2 \/ rad:", angles)
\n print("\u952e\u76d8\u52a8\u4f5c:", action)
\n print("\u5b9e\u9645\u672b\u7aef\u4f4d\u7f6e \/ m:", observation["robot0_eef_pos"])
\n print("\u76ee\u6807\u672b\u7aef\u4f4d\u7f6e \/ m:", target_pos)
\n last_log = time.perf_counter()<\/p>\n

assert np.isfinite(env.sim.data.qpos).all()<\/p>\n

remaining = 1 \/ 20 – (time.perf_counter() – start)
\n if remaining > 0:
\n time.sleep(remaining)<\/p>\n

print("A3_PIPER_KEYBOARD_TEST_ENDED")<\/p>\n

except KeyboardInterrupt:
\n print("\\\\n\u7528\u6237\u505c\u6b62\u6d4b\u8bd5\u3002")<\/p>\n

finally:
\n if device is not None:
\n device.listener.stop()
\n env.close()
\n cv2.destroyAllWindows()<\/p>\n

\u53ef\u4ee5\u770b\u5230ControlEnv\u5b9e\u4f8b\u5316\u65f6\u5df2\u80fd\u6210\u529f\u7528robots=["Piper"]\u505a\u53c2\u6570，\u8fd9\u4e00\u6b65\u4e4b\u540e\u4f1a\u7ee7\u7eed\u5f80\u4e0b\u4f20\u5230robosuite，\u5e76\u88ab\u6210\u529f\u8bc6\u522b\u3002<\/p>\n

LIBERO：ControlEnv(robots=["Piper"])
\n \u2193 \u4f20\u9012\u673a\u5668\u4eba\u540d\u79f0
\nrobosuite：\u67e5\u627e\u5e76\u521b\u5efa Piper，\u88c5\u4e0a PiperGripper \u548c\u63a7\u5236\u5668
\n \u2193 \u52a0\u8f7d\u3001\u7ec4\u88c5 XML
\nMuJoCo：\u8ba1\u7b97\u673a\u5668\u4eba\u8fd0\u52a8\u548c\u63a5\u89e6<\/p>\n

\u4e0b\u9762\u5206\u6790\u6700\u4e3a\u91cd\u8981\u7684\u63a7\u5236\u903b\u8f91\u90e8\u5206：<\/p>\n

candidate_pos = target_pos + action[:3] * POSITION_GAIN
\n if np.linalg.norm(candidate_pos – controller.ee_pos) <= MAX_POSITION_LEAD:
\n target_pos = candidate_pos<\/p>\n

delta_rotation = T.quat2mat(
\n T.axisangle2quat(action[3:6] * ROTATION_GAIN)
\n )
\n # \u540c\u6837，\u8f6c\u592a\u731b\u4e86\u5c31\u820d\u5f03，\u8fd9\u91cc\u6700\u597d\u63a2\u7a76\u6e05\u695a\u7ebf\u6027\u4ee3\u6570\u5173\u7cfb
\n # \u4e3a\u5565\u4e0d\u76f4\u63a5\u7528delta_rotation\u7b97rotation_gap？\u56e0\u4e3a\u5faa\u73af\u8dd1\u8d77\u6765\u4e4b\u540etarget_ori\u8ddfcontroller.ee_ori_mat\u4e0d\u4e00\u5b9a\u76f8\u7b49
\n candidate_ori = delta_rotation @ target_ori
\n rotation_gap = T.quat2axisangle(
\n T.mat2quat(candidate_ori @ controller.ee_ori_mat.T)
\n )
\n if np.linalg.norm(rotation_gap) <= MAX_ROTATION_LEAD:
\n target_ori = candidate_ori<\/p>\n

# action \u662f\u952e\u76d8\u589e\u91cf；command \u662f\u53d1\u7ed9 OSC \u7684\u7edd\u5bf9\u76ee\u6807\u4f4d\u59ff
\n command = np.concatenate([
\n target_pos,
\n T.quat2axisangle(T.mat2quat(target_ori)),
\n action[6:7],
\n ])<\/p>\n

action\u65f6\u662f\u952e\u76d8\u8bfb\u5230\u7684，\u8868\u793a\u6bcf\u6b21\u6309\u4e0b\u952e\u76d8\u540e\u76847\u7ef4\u53d8\u5316\u91cf，\u8fd9\u4e2a\u53d8\u5316\u91cf\u52a0\u4e0a\u4e0a\u4e00\u6b65\u7684\u671f\u671b\u76ee\u6807\u5c31\u7b49\u4e8e\u5f53\u524d\u8fd9\u4e00\u6b65\u7684\u671f\u671b\u76ee\u6807\u3002<\/p>\n

\u6ce8\u610f，\u4e3a\u4ec0\u4e48\u662f\u52a0\u4e0a\u4e00\u6b65\u7684\u671f\u671b\u76ee\u6807\u800c\u4e0d\u662f\u5f53\u524d\u7684\u771f\u5b9e\u72b6\u6001？\u56e0\u4e3a\u9700\u8981\u7ea0\u504f\u3002\u8bd5\u60f3，\u5047\u5982\u4f60\u957f\u6309\u952e\u76d8\u60f3\u8ba9\u673a\u68b0\u81c2\u5de6\u79fb\u5230A，\u4f46\u6b64\u65f6\u673a\u68b0\u81c2\u8d70\u504f\u5230\u4e86B，\u7531\u4e8e\u662f\u957f\u6309\u952e\u76d8，\u671f\u95f4\u7684action\u90fd\u662f0，\u5982\u679c\u662f\u57fa\u4e8e\u4e0a\u4e00\u6b65\u7684\u671f\u671b\u76ee\u6807，\u4e5f\u5c31\u662fA，\u90a3\u4e48\u673a\u68b0\u81c2\u6b64\u65f6\u5728B\u77e5\u9053\u8fd8\u9700\u8981\u7ee7\u7eed\u5f80A\u8d70，\u5b9e\u73b0\u7ea0\u504f；\u5982\u679c\u662f\u57fa\u4e8e\u5f53\u524d\u72b6\u6001，\u589e\u91cf\u4e3a0，\u90a3\u4e48\u673a\u68b0\u81c2\u6b64\u65f6\u7684\u76ee\u6807\u70b9\u4e5f\u5c31\u662fB\u70b9\u672c\u8eab，\u673a\u68b0\u81c2\u4e0d\u518d\u79fb\u52a8，\u65e0\u6cd5\u5230\u8fbe\u76ee\u6807\u70b9A\u3002<\/p>\n

\u65cb\u8f6c\u91cf\u4e5f\u662f\u540c\u6837\u7684\u7ea0\u504f\u601d\u60f3，\u8fd9\u91cc\u9700\u8981\u6ce8\u610f\u7684\u662f\u77e9\u9635\u65cb\u8f6c\u7684\u76f8\u5173\u77e5\u8bc6：<\/p>\n

1. \u65cb\u8f6c\u77e9\u9635\u5de6\u4e58\u5f53\u524d\u72b6\u6001\u77e9\u9635，\u4fbf\u80fd\u5f97\u5230\u65cb\u8f6c\u4e4b\u540e\u7684\u59ff\u6001\u77e9\u9635<\/p>\n

2. \u65cb\u8f6c\u77e9\u9635\u7684\u9006\u77e9\u9635\u7b49\u4e8e\u5176\u8f6c\u7f6e<\/p>\n

\n\u6700\u7ec8\u5b9e\u73b0\u4e86\u952e\u76d8\u63a7\u5236\u673a\u68b0\u81c2\u8fd0\u52a8，\u5e76\u5b8c\u6210\u4e86\u7269\u4f53\u5939\u53d6：<\/p>\n

\"\"<\/p>\n

\n

4. \u6570\u636e\u91c7\u96c6\u4e0e\u683c\u5f0f\u8f6c\u6362<\/h3>\n

4.1. \u91c7\u96c6\u811a\u672c<\/h4>\n

import argparse
\nimport hashlib
\nimport json
\nimport time
\nfrom datetime import datetime
\nfrom pathlib import Path
\nfrom zoneinfo import ZoneInfo<\/p>\n

import cv2
\nimport h5py
\nimport numpy as np<\/p>\n

from A4_2_piper_env import PACK, load_piper_env
\nfrom piper_xml import PHYSICS_XML_VERSION, fix_piper_xml<\/p>\n

from robosuite import macros
\nfrom robosuite.devices import Keyboard
\nfrom robosuite.utils import transform_utils as T
\nfrom robosuite.utils.input_utils import input2action
\nfrom libero.libero import benchmark<\/p>\n

parser = argparse.ArgumentParser(description="Piper \u8bad\u7ec3\u5f00\u5c40\u952e\u76d8\u91c7\u96c6，\u4e00\u6b21\u4e00\u6761")
\nparser.add_argument("–init-index", type=int, required=True, choices=range(30))
\nargs = parser.parse_args()<\/p>\n

CONTROL_FREQ = 20
\nMAX_STEPS = 1000
\nWRIST_FOV = 90.0<\/p>\n

# \u4fdd\u7559\u4e0a\u5468\u9a8c\u8bc1\u8fc7\u7684\u63a7\u5236\u53c2\u6570
\nPOSITION_GAIN = 0.01
\nROTATION_GAIN = 0.5
\nMAX_POSITION_LEAD = 0.03
\nMAX_ROTATION_LEAD = 0.2<\/p>\n

# \u201c\u8fd1\u9759\u6b62\u201d\u7684\u5224\u636e：\u4e00\u4e2a\u63a7\u5236\u6b65\u5185\u7684\u5b9e\u9645\u5173\u8282\u53d8\u5316
\nARM_STILL_THRESHOLD = 0.001 # rad
\nGRIPPER_STILL_THRESHOLD = 0.0001 # m<\/p>\n

OUTPUT_DIR = (
\n Path(__file__).resolve().parent
\n \/ "datasets\/b1_piper_black_bowl_v2"
\n)
\nINIT_SOURCE = f"{PACK.name}\/train.pruned_init"
\nINIT_SHA256 = hashlib.sha256((PACK \/ "train.pruned_init").read_bytes()).hexdigest()
\nINIT_MODEL_SHA256 = hashlib.sha256((PACK \/ "model.xml").read_bytes()).hexdigest()
\nMANIFEST = json.loads((PACK \/ "manifest.json").read_text(encoding="utf-8"))
\nassert len(MANIFEST["splits"]["train"]) == 30
\nRUN_ID = datetime.now(
\n ZoneInfo("Asia\/Shanghai")
\n).strftime("%Y%m%d_%H%M%S_%f")<\/p>\n

def display_image(frame, wrist=False):
\n """\u53ea\u6539\u53d8\u663e\u793a\u65b9\u5411，\u4e0d\u4fee\u6539\u539f\u59cb\u89c2\u6d4b\u3002"""
\n if macros.IMAGE_CONVENTION == "opengl":
\n frame = frame[::-1]
\n if wrist:
\n frame = np.rot90(frame, k=1)
\n return cv2.cvtColor(
\n np.ascontiguousarray(frame),
\n cv2.COLOR_RGB2BGR,
\n )<\/p>\n

class CollectKeyboard(Keyboard):
\n def __init__(self, **kwargs):
\n self.retry_count = 0
\n self.recording = False
\n super().__init__(**kwargs)<\/p>\n

def on_release(self, key):
\n # \u677e\u5f00 N \u65f6\u8ba1\u4e00\u6b21，\u907f\u514d\u957f\u6309\u91cd\u590d\u8ba1\u6570
\n if getattr(key, "char", None) == "n":
\n if self.recording:
\n self.retry_count += 1
\n print("\u624b\u52a8\u6807\u8bb0\u91cd\u8bd5\u6b21\u6570:", self.retry_count)
\n return
\n super().on_release(key)<\/p>\n

def show_progress():
\n """\u4ee5\u5b9e\u9645\u6210\u529f\u6587\u4ef6\u4e3a\u51c6；\u540c\u4e00\u65f6\u95f4\u53ea\u8fd0\u884c\u4e00\u4e2a\u91c7\u96c6\u8fdb\u7a0b\u3002"""
\n counts = [0] * 30
\n for path in sorted(OUTPUT_DIR.glob("demo_*.hdf5")):
\n with h5py.File(path, "r") as file:
\n info = json.loads(file["data"].attrs["env_info"])
\n demo = file["data\/demo_0"]
\n assert info["init_source"] == INIT_SOURCE, path
\n assert info["init_sha256"] == INIT_SHA256, path
\n assert info["init_model_sha256"] == INIT_MODEL_SHA256, path
\n index = info["init_index"]
\n assert 0 <= index < 30, path
\n assert bool(demo.attrs["success"]), path
\n assert 0 < len(demo["actions"]) <= MAX_STEPS, path
\n assert demo["actions"].shape == (len(demo["states"]), 7), path
\n counts[index] += 1
\n print("\u5404\u8bad\u7ec3\u5f00\u5c40\u6210\u529f\u6761\u6570（\u7f16\u53f7 0～29）:", counts)
\n print("\u7d2f\u8ba1\u6210\u529f\u6761\u6570:", sum(counts), "\/ 60")
\n print("\u5f53\u524d\u5f00\u5c40:", args.init_index, "\u5df2\u6210\u529f:", counts[args.init_index], "\/ 2")
\n return counts<\/p>\n

counts = show_progress()
\nif counts[args.init_index] >= 2:
\n parser.exit(message="\u5f53\u524d\u5f00\u5c40\u5df2\u6709\u4e24\u6761\u6210\u529f\u6f14\u793a，\u8bf7\u9009\u62e9\u5176\u4ed6\u7f16\u53f7\u3002\\\\n")<\/p>\n

def save_result(
\n states, actions, stationary_flags,
\n initial_state, model_xml, env_info, attempt,
\n):
\n """\u6210\u529f\u4e14\u672a\u8d85\u957f\u624d\u4fdd\u5b58 HDF5；\u6240\u6709\u5df2\u5f55\u5236\u5c1d\u8bd5\u90fd\u4fdd\u7559\u65e5\u5fd7\u3002"""
\n OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
\n path = None<\/p>\n

assert len(states) == len(actions) == len(stationary_flags)<\/p>\n

if attempt["success"]:
\n assert 0 < len(actions) <= MAX_STEPS<\/p>\n

state_array = np.asarray(states, dtype=np.float64)
\n action_array = np.asarray(actions, dtype=np.float64)<\/p>\n

assert action_array.shape == (len(state_array), 7)
\n assert np.isfinite(state_array).all()
\n assert np.isfinite(action_array).all()<\/p>\n

path = OUTPUT_DIR \/ f"demo_train_{args.init_index:02d}_{RUN_ID}.hdf5"
\n pending_path = path.with_suffix(".hdf5.partial")<\/p>\n

# \u5199\u5165\u4e2d\u65ad\u7684\u6587\u4ef6\u4e0d\u8ba1\u5165\u6210\u529f\u6761\u6570，\u539f\u6587\u4ef6\u59cb\u7ec8\u4e0d\u8986\u76d6\u3002
\n with h5py.File(pending_path, "x") as file:
\n data = file.create_group("data")
\n data.attrs["env"] = env_info["environment"]
\n data.attrs["env_info"] = json.dumps(
\n env_info, ensure_ascii=False
\n )<\/p>\n

demo = data.create_group("demo_0")
\n demo.create_dataset("states", data=state_array)
\n demo.create_dataset("actions", data=action_array)
\n demo.create_dataset(
\n "settled_initial_state", data=initial_state
\n )
\n # \u8bca\u65ad\u5b57\u6bb5，\u4e0d\u4f5c\u4e3a\u6a21\u578b\u8f93\u5165
\n demo.create_dataset(
\n "stationary_flags",
\n data=np.asarray(stationary_flags, dtype=bool),
\n )<\/p>\n

demo.attrs["model_file"] = model_xml
\n demo.attrs["success"] = True
\n demo.attrs["split"] = "train"
\n demo.attrs["init_index"] = args.init_index
\n demo.attrs["num_samples"] = len(actions)
\n demo.attrs["recording_seconds"] = attempt[
\n "recording_seconds"
\n ]
\n demo.attrs["collection_fps"] = attempt["collection_fps"]
\n demo.attrs["stationary_ratio"] = attempt[
\n "stationary_ratio"
\n ]
\n demo.attrs["retry_count_manual"] = attempt[
\n "retry_count_manual"
\n ]
\n demo.attrs["gripper_switch_count"] = attempt[
\n "gripper_switch_count"
\n ]<\/p>\n

with h5py.File(pending_path, "r") as file:
\n demo = file["data\/demo_0"]
\n assert demo["states"].shape == state_array.shape
\n assert demo["actions"].shape == action_array.shape
\n assert bool(demo.attrs["success"])
\n assert int(demo.attrs["init_index"]) == args.init_index
\n print("states shape:", demo["states"].shape)
\n print("actions shape:", demo["actions"].shape)<\/p>\n

pending_path.rename(path)
\n print("HDF5:", path)
\n print("\u6587\u4ef6\u5927\u5c0f \/ bytes:", path.stat().st_size)
\n print("B1_HDF5_READBACK_OK")
\n else:
\n print("\u672c\u6b21\u4e0d\u4fdd\u5b58\u4e3a\u6210\u529f\u793a\u8303，\u53ea\u8bb0\u5f55\u5c1d\u8bd5\u7ed3\u679c\u3002")<\/p>\n

attempt["hdf5_path"] = str(path) if path else None
\n attempt["env_info"] = env_info<\/p>\n

# ponytail: \u5355\u4e2a\u91c7\u96c6\u8fdb\u7a0b\u4f7f\u7528；\u5e76\u884c\u91c7\u96c6\u65f6\u9700\u72ec\u7acb\u65e5\u5fd7
\n with (OUTPUT_DIR \/ "attempts.jsonl").open(
\n "a", encoding="utf-8"
\n ) as file:
\n file.write(json.dumps(attempt, ensure_ascii=False) + "\\\\n")<\/p>\n

print("\\\\n=== B1 \u91c7\u96c6\u7ed3\u679c ===")
\n print("\u8bad\u7ec3\u5f00\u5c40\u7f16\u53f7:", args.init_index)
\n for key in [
\n "success", "steps", "recording_seconds",
\n "collection_fps", "stationary_ratio",
\n "gripper_switch_count", "retry_count_manual",
\n "stop_reason",
\n ]:
\n print(f"{key}: {attempt[key]}")
\n print("\u672c\u6b21\u5c1d\u8bd5\u65e5\u5fd7:", OUTPUT_DIR \/ "attempts.jsonl")
\n show_progress()<\/p>\n

env = None
\ndevice = None
\nstates = []
\nactions = []
\nstationary_flags = []<\/p>\n

record_start = None
\nsuccess = False
\nsuccess_count = 0
\ngripper_switch_count = 0
\nprevious_gripper_command = -1.0
\nstop_reason = "\u7528\u6237\u6309 Q \u7ed3\u675f"
\ninitial_state = None
\nmodel_xml = None
\nenv_info = None<\/p>\n

try:
\n env, observation, init_info = load_piper_env(
\n split="train", init_index=args.init_index, viewer=True,
\n )
\n suite = benchmark.get_benchmark_dict()[init_info["suite"]]()
\n task = suite.get_task(0)<\/p>\n

robot = env.robots[0]
\n controller = robot.controller
\n controller.update(force=True)
\n controller.uncoupling = False
\n controller.use_delta = False<\/p>\n

target_pos = controller.ee_pos.copy()
\n target_ori = controller.ee_ori_mat.copy()<\/p>\n

# A4 \u7684\u5f00\u5c40\u5df2\u7a33\u5b9a，\u4e0d\u989d\u5916\u9884\u70ed\u63a8\u8fdb；\u9996\u6b21\u52a8\u4f5c\u524d\u4ecd\u6309\u539f\u903b\u8f91\u4fdd\u6301\u59ff\u6001\u3002
\n initial_state = env.get_sim_state().copy()
\n np.testing.assert_allclose(
\n observation["akita_black_bowl_1_pos"],
\n init_info["expected_position"], rtol=0, atol=1e-7,
\n )
\n initial_objects = {
\n key: np.asarray(value).tolist()
\n for key, value in observation.items()
\n if not key.startswith("robot0_")
\n and key.endswith(("_pos", "_quat"))
\n }<\/p>\n

camera_id = env.sim.model.camera_name2id(
\n "robot0_eye_in_hand"
\n )
\n assert np.isclose(env.sim.model.cam_fovy[camera_id], WRIST_FOV)
\n camera_config = {
\n "pos": env.sim.model.cam_pos[camera_id].tolist(),
\n "quat": env.sim.model.cam_quat[camera_id].tolist(),
\n "fovy": float(env.sim.model.cam_fovy[camera_id]),
\n }
\n # \u4e0e A4 \u6062\u590d\u5f00\u5c40\u65f6\u4f7f\u7528\u540c\u4e00\u4efd XML；\u76f8\u673a\u5df2\u56fa\u5b9a 90\u00b0，\u4e0d\u518d\u91cd\u590d\u8c03\u53c2\u622a\u56fe\u3002
\n model_xml = fix_piper_xml((PACK \/ "model.xml").read_text(encoding="utf-8"))<\/p>\n

env_info = {
\n "environment": type(env.env).__name__,
\n "physics_xml_version": PHYSICS_XML_VERSION,
\n "suite": init_info["suite"],
\n "task_id": 0,
\n "task": task.language,
\n "bddl_file": task.bddl_file,
\n "robots": ["Piper"],
\n "robot_model": type(robot.robot_model).__name__,
\n "seed": MANIFEST["seed"],
\n "split": "train",
\n "init_source": INIT_SOURCE,
\n "init_sha256": INIT_SHA256,
\n "init_model_sha256": INIT_MODEL_SHA256,
\n "init_index": init_info["init_index"],
\n "expected_initial_bowl_position": init_info["expected_position"],
\n "initial_objects": initial_objects,
\n "control_freq": CONTROL_FREQ,
\n "physics_dt": float(env.sim.model.opt.timestep),
\n "controller": {
\n "type": controller.name,
\n "control_delta": False,
\n "uncouple_pos_ori": False,
\n "kp": controller.kp.tolist(),
\n "kd": controller.kd.tolist(),
\n "input_min": controller.input_min.tolist(),
\n "input_max": controller.input_max.tolist(),
\n "output_min": controller.output_min.tolist(),
\n "output_max": controller.output_max.tolist(),
\n },
\n "action_representation": (
\n "world_xyz_m + world_axis_angle_rad + gripper"
\n ),
\n "wrist_camera": camera_config,
\n "wrist_display_rotation_deg": 90,
\n "stationary_definition": {
\n "max_arm_joint_change_rad": ARM_STILL_THRESHOLD,
\n "max_gripper_joint_change_m": GRIPPER_STILL_THRESHOLD,
\n },
\n }<\/p>\n

device = CollectKeyboard(
\n pos_sensitivity=0.1,
\n rot_sensitivity=0.2,
\n )
\n device.start_control()<\/p>\n

print("\\\\n=== B1 Piper \u8bad\u7ec3\u6570\u636e\u91c7\u96c6 ===")
\n print("\u4efb\u52a1:", task.language)
\n print("\u8bad\u7ec3\u5f00\u5c40\u7f16\u53f7:", args.init_index)
\n print("\u76ee\u6807\u7897\u521d\u59cb\u4f4d\u7f6e:", init_info["expected_position"])
\n print("\u7269\u7406\u6a21\u578b\u7248\u672c:", init_info["physics_xml_version"])
\n print("\u76ee\u6807\u7897\u8d28\u91cf \/ kg:", init_info["target_bowl_mass_kg"])
\n print("\u8155\u90e8\u76f8\u673a:", camera_config)
\n print("\u8f93\u51fa\u76ee\u5f55:", OUTPUT_DIR)
\n print("\u9996\u6b21\u8fd0\u52a8\u6216\u95ed\u5408\u5939\u722a\u5f00\u59cb\u5f55\u5236\u3002")
\n print("\u79fb\u52a8\u3001\u65cb\u8f6c\u548c\u5939\u722a\u6309\u952e\u6cbf\u7528\u4e0a\u5468\u3002")
\n print("N：\u5931\u8d25\u540e\u91cd\u65b0\u5c1d\u8bd5\u65f6，\u624b\u52a8\u6807\u8bb0\u4e00\u6b21\u91cd\u8bd5\u3002")
\n print("Q：\u653e\u5f03；\u660e\u663e\u5931\u63a7\u65f6 Ctrl+C\u3002")
\n print("\u8fbe\u5230 1000 \u6b65\u4ecd\u672a\u6210\u529f\u5219\u7ec8\u6b62\u3002")<\/p>\n

last_log = time.perf_counter()<\/p>\n

while True:
\n loop_start = time.perf_counter()
\n action, _ = input2action(
\n device=device,
\n robot=robot,
\n active_arm="right",
\n )
\n if action is None:
\n break<\/p>\n

assert action.shape == (7,)
\n assert np.isfinite(action).all()
\n controller.update(force=True)<\/p>\n

candidate_pos = target_pos + action[:3] * POSITION_GAIN
\n if np.linalg.norm(
\n candidate_pos – controller.ee_pos
\n ) <= MAX_POSITION_LEAD:
\n target_pos = candidate_pos<\/p>\n

delta_rotation = T.quat2mat(
\n T.axisangle2quat(action[3:6] * ROTATION_GAIN)
\n )
\n candidate_ori = delta_rotation @ target_ori
\n rotation_gap = T.quat2axisangle(
\n T.mat2quat(candidate_ori @ controller.ee_ori_mat.T)
\n )
\n if np.linalg.norm(rotation_gap) <= MAX_ROTATION_LEAD:
\n target_ori = candidate_ori<\/p>\n

command = np.concatenate([
\n target_pos,
\n T.quat2axisangle(T.mat2quat(target_ori)),
\n action[6:7],
\n ])
\n assert np.isfinite(command).all()<\/p>\n

if record_start is None and (
\n np.any(action[:6] != 0) or action[6] > 0
\n ):
\n record_start = time.perf_counter()
\n device.recording = True
\n print("RECORDING_STARTED")<\/p>\n

if record_start is not None:
\n state_before = env.get_sim_state().copy()
\n arm_before = observation["robot0_joint_pos"].copy()
\n gripper_before = observation[
\n "robot0_gripper_qpos"
\n ].copy()<\/p>\n

observation, _, _, _ = env.step(command)<\/p>\n

if record_start is not None:
\n states.append(state_before)
\n actions.append(command.copy())<\/p>\n

arm_change = np.max(np.abs(
\n observation["robot0_joint_pos"] – arm_before
\n ))
\n gripper_change = np.max(np.abs(
\n observation["robot0_gripper_qpos"]
\n – gripper_before
\n ))
\n stationary_flags.append(bool(
\n arm_change <= ARM_STILL_THRESHOLD
\n and gripper_change <= GRIPPER_STILL_THRESHOLD
\n ))<\/p>\n

if command[6] != previous_gripper_command:
\n gripper_switch_count += 1
\n previous_gripper_command = float(command[6])<\/p>\n

assert np.isfinite(env.sim.data.qpos).all()
\n env.env.render()<\/p>\n

for key in [
\n "agentview_image",
\n "robot0_eye_in_hand_image",
\n ]:
\n assert observation[key].shape == (256, 256, 3)
\n cv2.imshow(
\n key,
\n display_image(
\n observation[key],
\n wrist=(key == "robot0_eye_in_hand_image"),
\n ),
\n )
\n cv2.waitKey(1)<\/p>\n

if record_start is not None:
\n success_count = (
\n success_count + 1
\n if env.check_success() and command[6] < 0
\n else 0
\n )
\n if success_count >= 10:
\n success = True
\n stop_reason = "\u8fde\u7eed\u6210\u529f\u5e76\u6301\u7eed\u53d1\u9001\u677e\u722a\u6307\u4ee4"
\n print("TASK_SUCCESS")
\n break<\/p>\n

if len(actions) >= MAX_STEPS:
\n stop_reason = "\u8fbe\u5230 1000 \u6b65，\u4ecd\u672a\u6ee1\u8db3\u6210\u529f\u6761\u4ef6"
\n break<\/p>\n

if time.perf_counter() – last_log >= 1:
\n print(
\n "\u6b65\u6570:", len(actions),
\n "\u5939\u722a\u5207\u6362:", gripper_switch_count,
\n "\u624b\u52a8\u91cd\u8bd5:", device.retry_count,
\n "\u8fde\u7eed\u6210\u529f:", success_count,
\n )
\n last_log = time.perf_counter()<\/p>\n

remaining = (
\n 1 \/ CONTROL_FREQ
\n – (time.perf_counter() – loop_start)
\n )
\n if remaining > 0:
\n time.sleep(remaining)<\/p>\n

except KeyboardInterrupt:
\n success = False
\n stop_reason = "\u7528\u6237\u901a\u8fc7 Ctrl+C \u7ed3\u675f"<\/p>\n

except Exception as error:
\n success = False
\n stop_reason = f"{type(error).__name__}: {error}"
\n raise<\/p>\n

finally:
\n record_end = time.perf_counter()<\/p>\n

try:
\n if record_start is None:
\n print("\u6ca1\u6709\u5f00\u59cb\u5f55\u5236，\u4e0d\u8ba1\u5165\u91c7\u96c6\u5c1d\u8bd5\u3002")
\n else:
\n elapsed = record_end – record_start
\n attempt = {
\n "timestamp": RUN_ID,
\n "split": "train",
\n "init_index": args.init_index,
\n "success": success,
\n "steps": len(actions),
\n "recording_seconds": elapsed,
\n "collection_fps": (
\n len(actions) \/ elapsed if elapsed > 0 else 0.0
\n ),
\n "stationary_ratio": (
\n float(np.mean(stationary_flags))
\n if stationary_flags else None
\n ),
\n "gripper_switch_count": gripper_switch_count,
\n "retry_count_manual": (
\n device.retry_count if device else None
\n ),
\n "stop_reason": stop_reason,
\n }
\n save_result(
\n states, actions, stationary_flags,
\n initial_state, model_xml, env_info, attempt,
\n )
\n finally:
\n try:
\n if device is not None:
\n device.listener.stop()
\n finally:
\n try:
\n if env is not None:
\n env.close()
\n finally:
\n cv2.destroyAllWindows()<\/p>\n

\u672c\u6b21\u5b9a\u4e49\u4e8630\u79cd\u521d\u59cb\u72b6\u6001，\u4e5f\u5c31\u662f30\u79cd\u7897\u7684\u521d\u59cb\u4f4d\u7f6e，\u5404\u81ea\u91c7\u4e24\u6761\u6570\u636e，\u4e00\u517160\u6761\u3002<\/p>\n

4.2. \u7edd\u5bf9\u76ee\u6807\u4e0e\u76f8\u5bf9\u8f93\u5165\u4e4b\u524d\u7684\u6570\u91cf\u5173\u7cfb<\/h4>\n

\u8fd9\u4e00\u6b65\u6d89\u53ca\u5230\u6570\u636e\u7684\u8f6c\u6362\u3002\u6211\u4eec\u5728\u91c7\u96c6\u6570\u636e\u65f6，\u5f97\u5230\u7684\u662f\u7edd\u5bf9\u4f4d\u59ff，\u6bd4\u5982\u7b2c0\u79d2\u672b\u7aef\u57280.2\u7c73，\u7b2c1\u79d2\u57280.21\u79d2，\u8fd91\u79d2\u95f4\u7684\u53d8\u5316\u91cf\u4e3a0.01\u7c73，\u6a21\u578b\u9700\u8981\u76847\u7ef4action\u4e2d\u6bcf\u4e00\u4f4d\u90fd\u662f\u76f8\u5bf9\u589e\u91cf，\u90a3\u4e5f\u5c31\u662f\u8bf4\u6b64\u65f6\u586b\u5165action\u7684\u6570\u503c\u5c31\u662f0.1\u5417？\u5b9e\u9645\u5e76\u4e0d\u662f，\u5b9e\u9645\u586b\u5165\u7684\u6570\u503c\u8ddf\u8fd9\u91cc\u76840.1\u5b58\u5728\u500d\u6570\u5173\u7cfb，\u5177\u4f53\u6570\u636e\u7531\u4e0b\u9762\u7684\u4ee3\u7801\u5f97\u77e5：<\/p>\n

import sys
\nfrom pathlib import Path
\nimport inspect
\nimport numpy as np<\/p>\n

week4_dir = Path("\/home\/dukiya\/libero-learning\/week4")
\nassert (week4_dir \/ "piper_robot.py").is_file()
\n# \u628a week4 \u76ee\u5f55\u52a0\u8fdb sys.path，\u8ba9 Python \u80fd\u627e\u5230 piper_robot.py\u3002
\nsys.path.insert(0, str(week4_dir))<\/p>\n

import piper_robot
\nfrom libero.libero import benchmark, get_libero_path
\nfrom libero.libero.envs.env_wrapper import ControlEnv
\n# \u59ff\u6001\u8f6c\u6362\u5de5\u5177
\nfrom robosuite.utils import transform_utils as T<\/p>\n

suite = benchmark.get_benchmark_dict()["libero_spatial"]()
\ntask = suite.get_task(0)<\/p>\n

bddl_path = (
\n Path(get_libero_path("bddl_files"))
\n \/ task.problem_folder
\n \/ task.bddl_file
\n)<\/p>\n

env = ControlEnv(
\n bddl_file_name=str(bddl_path),
\n robots=["Piper"],
\n controller="OSC_POSE",
\n initialization_noise=None,
\n control_freq=20,
\n # \u4e0d\u5f00\u7a97\u53e3
\n has_renderer=False,
\n has_offscreen_renderer=False,
\n # \u89c2\u6d4b\u6570\u636e\u4e0d\u8fd4\u56de\u56fe\u50cf
\n use_camera_obs=False,
\n)<\/p>\n

try:
\n env.seed(1000)
\n env.reset()<\/p>\n

# \u62ff\u5230\u63a7\u5236\u5668，\u4f4d\u7f6e\u548c\u59ff\u6001\u8054\u5408\u8ba1\u7b97
\n controller = env.robots[0].controller
\n controller.uncoupling = False
\n controller.update(force=True)<\/p>\n

current_pos = controller.ee_pos.copy()
\n current_ori = controller.ee_ori_mat.copy()
\n # \u4eff\u771f\u5668\u5185\u7684\u6570\u636e
\n original_qpos = env.sim.data.qpos.copy()
\n original_time = env.sim.data.time<\/p>\n

# \u770b\u4e00\u4e0b\u7f29\u653e\u7cfb\u6570\u662f\u591a\u5c11
\n # \u7269\u7406\u4f4d\u79fb = output_min + (action – input_min) \/ (input_max – input_min) \u00d7 (output_max – output_min)
\n print("=== \u63a7\u5236\u5668\u914d\u7f6e ===")
\n print("\u6e90\u7801\u6587\u4ef6:", inspect.getfile(type(controller)))
\n print("input_min:", controller.input_min)
\n print("input_max:", controller.input_max)
\n print("output_min:", controller.output_min)
\n print("output_max:", controller.output_max)
\n print("impedance_mode:", controller.impedance_mode)<\/p>\n

assert controller.control_dim == 6
\n assert controller.impedance_mode == "fixed"<\/p>\n

target_pos = current_pos + np.array([0.01, -0.005, 0.003])<\/p>\n

# \u8f74\u89d2 \u2192 \u56db\u5143\u6570 \u2192 \u65cb\u8f6c\u77e9\u9635，\u5de6\u4e58\u5f53\u524d\u59ff\u6001
\n rotation_change = np.array([0.02, -0.03, 0.04])
\n target_ori = (
\n T.quat2mat(T.axisangle2quat(rotation_change))
\n @ current_ori
\n )<\/p>\n

# \u7edd\u5bf9action\u503c：\u4f4d\u7f6e+\u59ff\u6001\u8f74\u89d2+\u5939\u722a\u662f\u5426\u5f00\u5408
\n absolute_action = np.concatenate([
\n target_pos,
\n T.quat2axisangle(T.mat2quat(target_ori)),
\n [-1.0],
\n ])<\/p>\n

# False:\u8f93\u5165\u7684\u662f\u7edd\u5bf9\u76ee\u6807；True:\u8f93\u5165\u7684\u662f\u76f8\u5bf9\u589e\u91cf
\n controller.use_delta = False
\n # \u91cd\u65b0\u8bbe\u5b9a\u76ee\u6807\u70b9，\u53ea\u53d6\u524d6\u7ef4，\u56e0\u4e3a\u5939\u722a\u662f\u5355\u72ec\u5904\u7406\u7684
\n controller.reset_goal()
\n controller.set_goal(absolute_action[:6])<\/p>\n

absolute_goal_pos = controller.goal_pos.copy()
\n absolute_goal_ori = controller.goal_ori.copy()<\/p>\n

# \u5f00\u59cb\u8ba1\u7b97\u76f8\u5bf9\u6539\u53d8\u91cf
\n position_delta = target_pos – current_pos
\n # \u8ba1\u7b97\u4ece\u5f53\u524d\u59ff\u6001\u5230\u76ee\u6807\u59ff\u6001\u7684\u76f8\u5bf9\u65cb\u8f6c，\u5148\u7b97\u65cb\u8f6c\u77e9\u9635，\u518d\u8f6c\u89d2
\n # target_ori， current_ori\u90fd\u662f\u77e9\u9635，\u65cb\u8f6c\u77e9\u9635\u7684\u8f6c\u7f6e\u7b49\u4e8e\u5176\u9006
\n relative_rotation = target_ori @ current_ori.T
\n rotation_delta = T.quat2axisangle(
\n T.mat2quat(relative_rotation)
\n )<\/p>\n

physical_delta = np.concatenate([
\n position_delta,
\n rotation_delta,
\n ])<\/p>\n

# \u53cd\u7b97\u63a7\u5236\u5668\u7f29\u653e\u524d\u7684\u8f93\u5165，\u4e0d\u628a\u201c\u7c73\u201d\u76f4\u63a5\u5f53\u52a8\u4f5c\u8f93\u5165
\n input_mid = (
\n controller.input_max + controller.input_min
\n ) \/ 2<\/p>\n

output_mid = (
\n controller.output_max + controller.output_min
\n ) \/ 2<\/p>\n

scale = (
\n (controller.output_max – controller.output_min)
\n \/ (controller.input_max – controller.input_min)
\n )
\n assert np.all(scale > 0)<\/p>\n

relative_arm_action = (
\n (physical_delta – output_mid) \/ scale + input_mid
\n )<\/p>\n

# \u672c\u5b9e\u9a8c\u9009\u62e9\u7684\u5c0f\u76ee\u6807\u5e94\u5728\u8f93\u5165\u8303\u56f4\u5185，\u4e0d\u80fd\u9760\u88c1\u526a\u63a9\u76d6\u9519\u8bef
\n assert np.all(
\n relative_arm_action >= controller.input_min – 1e-8
\n )
\n assert np.all(
\n relative_arm_action <= controller.input_max + 1e-8
\n )<\/p>\n

# \u673a\u68b0\u81c2+\u5939\u722a
\n relative_action = np.concatenate([
\n relative_arm_action,
\n [-1.0],
\n ])<\/p>\n

controller.use_delta = True
\n controller.reset_goal()
\n controller.set_goal(relative_action[:6])<\/p>\n

# \u8ba1\u7b97\u76f8\u5bf9\u52a8\u4f5c\u548c\u7edd\u5bf9\u52a8\u4f5c\u4e4b\u95f4\u7684\u8bef\u5dee
\n position_error = np.linalg.norm(
\n controller.goal_pos – absolute_goal_pos
\n )
\n rotation_matrix_error = np.linalg.norm(
\n controller.goal_ori – absolute_goal_ori
\n )<\/p>\n

print("\\\\n=== \u540c\u4e00\u76ee\u6807\u7684\u4e24\u79cd\u8f93\u5165 ===")
\n print("\u7edd\u5bf9\u52a8\u4f5c:", absolute_action)
\n print("\u5b9e\u9645\u4f4d\u59ff\u589e\u91cf:", physical_delta)
\n print("relative \u52a8\u4f5c:", relative_action)<\/p>\n

print("\\\\n=== \u63a7\u5236\u5668\u751f\u6210\u7684\u76ee\u6807\u662f\u5426\u4e00\u81f4 ===")
\n print("\u4f4d\u7f6e\u8bef\u5dee \/ m:", position_error)
\n print("\u65cb\u8f6c\u77e9\u9635\u5dee\u7684\u8303\u6570:", rotation_matrix_error)<\/p>\n

assert absolute_action.shape == relative_action.shape == (7,)
\n assert np.isfinite(relative_action).all()
\n assert position_error < 1e-6
\n assert rotation_matrix_error < 1e-6<\/p>\n

# \u786e\u8ba4\u6ca1\u6709\u63a8\u8fdb\u7269\u7406\u65f6\u95f4，\u4e5f\u6ca1\u6709\u6539\u53d8\u5173\u8282\u4f4d\u7f6e
\n assert env.sim.data.time == original_time
\n assert np.array_equal(env.sim.data.qpos, original_qpos)<\/p>\n

print("\\\\n=== \u672c\u673a set_goal \u6e90\u7801 ===")
\n print(inspect.getsource(type(controller).set_goal))
\n print("A1_ACTION_MAPPING_OK")<\/p>\n

finally:
\n env.close()<\/p>\n

\u8f93\u51fa：<\/p>\n

(libero) dukiya@dukiya-XiaoXinPro-14-APH8:~\/libero-learning$ \/home\/dukiya\/venvs\/libero\/bin\/python \/home\/dukiya\/libero-learning\/week5\/A1_3_check_action_mapping.py
\n[robosuite WARNING] No private macro file found! (__init__.py:7)
\n[robosuite WARNING] It is recommended to use a private macro file (__init__.py:8)
\n[robosuite WARNING] To setup, run: python \/home\/dukiya\/venvs\/libero\/lib\/python3.10\/site-packages\/robosuite\/scripts\/setup_macros.py (__init__.py:9)
\n[info] using task orders [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
\nLocal assets not found. Downloading from HuggingFace Hub…
\nAssets already downloaded at \/home\/dukiya\/.cache\/libero\/assets
\n=== \u63a7\u5236\u5668\u914d\u7f6e ===
\n\u6e90\u7801\u6587\u4ef6: \/home\/dukiya\/venvs\/libero\/lib\/python3.10\/site-packages\/robosuite\/controllers\/osc.py
\ninput_min: [-1. -1. -1. -1. -1. -1.]
\ninput_max: [1. 1. 1. 1. 1. 1.]
\noutput_min: [-0.05 -0.05 -0.05 -0.5 -0.5 -0.5 ]
\noutput_max: [0.05 0.05 0.05 0.5 0.5 0.5 ]
\nimpedance_mode: fixed<\/p>\n

=== \u540c\u4e00\u76ee\u6807\u7684\u4e24\u79cd\u8f93\u5165 ===
\n\u7edd\u5bf9\u52a8\u4f5c: [ 1.54273714e-04 -3.58575396e-03 1.21355331e+00 -2.85290014e-02
\n 2.02465343e+00 4.51160483e-02 -1.00000000e+00]
\n\u5b9e\u9645\u4f4d\u59ff\u589e\u91cf: [ 0.01 -0.005 0.003 0.01999984 -0.02999977 0.03999976]
\nrelative \u52a8\u4f5c: [ 0.2 -0.1 0.06 0.03999967 -0.05999953 0.07999952
\n -1. ]<\/p>\n

=== \u63a7\u5236\u5668\u751f\u6210\u7684\u76ee\u6807\u662f\u5426\u4e00\u81f4 ===
\n\u4f4d\u7f6e\u8bef\u5dee \/ m: 0.0
\n\u65cb\u8f6c\u77e9\u9635\u5dee\u7684\u8303\u6570: 6.252380293787821e-07<\/p>\n

=== \u672c\u673a set_goal \u6e90\u7801 ===
\n def set_goal(self, action, set_pos=None, set_ori=None):
\n """
\n Sets goal based on input @action. If self.impedance_mode is not "fixed", then the input will be parsed into the
\n delta values to update the goal position \/ pose and the kp and\/or damping_ratio values to be immediately updated
\n internally before executing the proceeding control loop.<\/p>\n

Note that @action expected to be in the following format, based on impedance mode!<\/p>\n

:Mode `'fixed'`: [joint pos command]
\n :Mode `'variable'`: [damping_ratio values, kp values, joint pos command]
\n :Mode `'variable_kp'`: [kp values, joint pos command]<\/p>\n

Args:
\n action (Iterable): Desired relative joint position goal state
\n set_pos (Iterable): If set, overrides @action and sets the desired absolute eef position goal state
\n set_ori (Iterable): IF set, overrides @action and sets the desired absolute eef orientation goal state
\n """
\n # Update state
\n self.update()<\/p>\n

# Parse action based on the impedance mode, and update kp \/ kd as necessary
\n if self.impedance_mode == "variable":
\n damping_ratio, kp, delta = action[:6], action[6:12], action[12:]
\n self.kp = np.clip(kp, self.kp_min, self.kp_max)
\n self.kd = 2 * np.sqrt(self.kp) * np.clip(damping_ratio, self.damping_ratio_min, self.damping_ratio_max)
\n elif self.impedance_mode == "variable_kp":
\n kp, delta = action[:6], action[6:]
\n self.kp = np.clip(kp, self.kp_min, self.kp_max)
\n self.kd = 2 * np.sqrt(self.kp) # critically damped
\n else: # This is case "fixed"
\n delta = action<\/p>\n

# If we're using deltas, interpret actions as such
\n if self.use_delta:
\n if delta is not None:
\n scaled_delta = self.scale_action(delta)
\n if not self.use_ori and set_ori is None:
\n # Set default control for ori since user isn't actively controlling ori
\n set_ori = np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, -1.0]])
\n else:
\n scaled_delta = []
\n # Else, interpret actions as absolute values
\n else:
\n if set_pos is None:
\n set_pos = delta[:3]
\n # Set default control for ori if we're only using position control
\n if set_ori is None:
\n set_ori = (
\n T.quat2mat(T.axisangle2quat(delta[3:6]))
\n if self.use_ori
\n else np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, -1.0]])
\n )
\n # No scaling of values since these are absolute values
\n scaled_delta = delta<\/p>\n

# We only want to update goal orientation if there is a valid delta ori value OR if we're using absolute ori
\n # use math.isclose instead of numpy because numpy is slow
\n bools = [0.0 if math.isclose(elem, 0.0) else 1.0 for elem in scaled_delta[3:]]
\n if sum(bools) > 0.0 or set_ori is not None:
\n self.goal_ori = set_goal_orientation(
\n scaled_delta[3:], self.ee_ori_mat, orientation_limit=self.orientation_limits, set_ori=set_ori
\n )
\n self.goal_pos = set_goal_position(
\n scaled_delta[:3], self.ee_pos, position_limit=self.position_limits, set_pos=set_pos
\n )<\/p>\n

if self.interpolator_pos is not None:
\n self.interpolator_pos.set_goal(self.goal_pos)<\/p>\n

if self.interpolator_ori is not None:
\n self.ori_ref = np.array(self.ee_ori_mat) # reference is the current orientation at start
\n self.interpolator_ori.set_goal(
\n orientation_error(self.goal_ori, self.ori_ref)
\n ) # goal is the total orientation error
\n self.relative_ori = np.zeros(3) # relative orientation always starts at 0<\/p>\n

A1_ACTION_MAPPING_OK<\/p>\n

\u91cd\u70b9\u5728\u8fd9\u91cc：<\/p>\n

\u5b9e\u9645\u4f4d\u59ff\u589e\u91cf: [ 0.01 -0.005 0.003 0.01999984 -0.02999977 0.03999976]
\nrelative \u52a8\u4f5c: [ 0.2 -0.1 0.06 0.03999967 -0.05999953 0.07999952
\n -1. ]<\/p>\n

\u53ef\u4ee5\u53d1\u73b0，\u5b9e\u9645\u7684\u589e\u91cf\u4e3a0.01，\u4f46\u586b\u5165action\u7684\u76f8\u5bf9\u91cf\u4e3a0.2\u3002\u7b80\u5355\u6765\u8bf4，\u5c31\u662f\u6211\u4eec\u5728\u8f6c\u6362\u6570\u636e\u65f6，\u9700\u8981\u628a\u539f\u59cb\u6570\u636e\u6bcf\u4e00\u5e27\u7684\u53d8\u5316\u91cf\u6c42\u51fa\u6765，\u7136\u540e\u4e58\u4e0a20，\u624d\u80fd\u53d8\u6210\u6a21\u578b\u9700\u8981\u7684\u6570\u636e\u3002<\/p>\n

4.3. \u6570\u636e\u8f6c\u6362<\/h4>\n

\u57fa\u4e8e4.2，\u6211\u4eec\u8981\u5c06\u81ea\u5df1\u91c7\u7684\u539f\u59cbhdf5\u8f6c\u6362\u6210\u6a21\u578b\u6240\u9700\u8981\u8868\u5f81\u65b9\u5f0f\u7684hdf5，\u518d\u8f6c\u6210\u8bad\u7ec3\u6240\u9700\u8981\u7684lerobot\u683c\u5f0f\u3002<\/p>\n

4.3.1. \u539f\u59cbhdf5\u8f6c\u4e2d\u95f4hdf5<\/h5>\n

import sys
\nimport json
\nimport time
\nfrom pathlib import Path<\/p>\n

import cv2
\nimport h5py
\nimport numpy as np
\nfrom piper_xml import PHYSICS_XML_VERSION, fix_piper_xml<\/p>\n

# \u590d\u7528\u5df2\u7ecf\u9002\u914d\u597d\u7684 Piper
\nsys.path.insert(0, "\/home\/dukiya\/libero-learning\/week4")
\nimport piper_robot<\/p>\n

from robosuite import macros
\nfrom robosuite.utils import transform_utils as T
\nfrom libero.libero.envs.env_wrapper import ControlEnv<\/p>\n

SOURCE = Path(
\n "\/home\/dukiya\/libero-learning\/week5\/datasets\/"
\n "a2_piper_black_bowl\/"
\n "demo_20261015_234043_644558.hdf5"
\n)<\/p>\n

# \u6bcf\u6b21\u4f7f\u7528\u65b0\u76ee\u5f55，\u907f\u514d\u8986\u76d6\u4e4b\u524d\u7684\u7ed3\u679c
\nOUTPUT = (
\n Path(__file__).resolve().parent
\n \/ "datasets\/a3_replay"
\n \/ f"{SOURCE.stem}_{time.time_ns()}"
\n)<\/p>\n

CAMERAS = {
\n "agentview_image": "observation.images.image",
\n "robot0_eye_in_hand_image": "observation.images.image2",
\n}<\/p>\n

def absolute_to_relative(action, controller):
\n """\u628a\u4e16\u754c\u5750\u6807\u4e0b\u7684\u7edd\u5bf9\u76ee\u6807，\u8f6c\u6362\u4e3a\u63a7\u5236\u5668\u7f29\u653e\u524d\u7684\u76f8\u5bf9\u52a8\u4f5c\u3002"""
\n target_rotation = T.quat2mat(
\n T.axisangle2quat(action[3:6])
\n )<\/p>\n

position_delta = action[:3] – controller.ee_pos<\/p>\n

# \u76f8\u5bf9\u65cb\u8f6c\u5fc5\u987b\u7528\u77e9\u9635\u8ba1\u7b97，\u4e0d\u80fd\u76f4\u63a5\u76f8\u51cf\u4e24\u4e2a\u8f74\u89d2
\n rotation_delta = (
\n target_rotation @ controller.ee_ori_mat.T
\n )
\n quat_delta = T.mat2quat(rotation_delta).astype(np.float64)<\/p>\n

# \u5bf9\u589e\u91cf\u9009\u62e9\u8f83\u77ed\u7684\u65cb\u8f6c；\u4e0d\u6539\u53d8 observation.state \u7684\u56db\u5143\u6570\u7ea6\u5b9a
\n if quat_delta[3] < 0:
\n quat_delta = -quat_delta<\/p>\n

# mat2quat \u4f7f\u7528 float32；\u6781\u5c0f\u65cb\u8f6c\u7684 w \u53ef\u80fd\u820d\u5165\u4e3a 1\u3002
\n # \u7528 atan2 \u4fdd\u7559\u5411\u91cf\u90e8\u5206\u643a\u5e26\u7684\u5c0f\u65cb\u8f6c，\u907f\u514d acos(w) \u5c06\u5176\u5f52\u96f6\u3002
\n vector = quat_delta[:3]
\n vector_norm = np.linalg.norm(vector)
\n if vector_norm > 1e-12:
\n angle = 2.0 * np.arctan2(vector_norm, quat_delta[3])
\n rotation_vector = vector \/ vector_norm * angle
\n else:
\n rotation_vector = 2.0 * vector<\/p>\n

physical_delta = np.concatenate([
\n position_delta,
\n rotation_vector,
\n ])<\/p>\n

input_mid = (
\n controller.input_max + controller.input_min
\n ) \/ 2<\/p>\n

output_mid = (
\n controller.output_max + controller.output_min
\n ) \/ 2<\/p>\n

scale = (
\n (controller.output_max – controller.output_min)
\n \/ (controller.input_max – controller.input_min)
\n )
\n assert np.all(scale > 0)<\/p>\n

relative_arm = (
\n (physical_delta – output_mid) \/ scale + input_mid
\n )<\/p>\n

# \u4e0d\u9759\u9ed8\u88c1\u526a，\u5426\u5219\u53ef\u80fd\u63a9\u76d6\u8f6c\u6362\u9519\u8bef
\n if (
\n np.any(relative_arm < controller.input_min – 1e-6)
\n or np.any(relative_arm > controller.input_max + 1e-6)
\n ):
\n raise ValueError(
\n f"\u76f8\u5bf9\u52a8\u4f5c\u8d85\u8fc7\u63a7\u5236\u5668\u8f93\u5165\u8303\u56f4: {relative_arm}"
\n )<\/p>\n

return np.concatenate([
\n relative_arm,
\n action[6:7], # \u4fdd\u7559\u5939\u722a\u5f00\u5408\u6307\u4ee4
\n ])<\/p>\n

start = time.perf_counter()<\/p>\n

# \u53ea\u8bfb\u539f\u59cb\u6f14\u793a，\u4e0d\u4fee\u6539\u91c7\u96c6\u6570\u636e
\nwith h5py.File(SOURCE, "r") as file:
\n demo = file["data\/demo_0"]<\/p>\n

assert bool(demo.attrs["success"])
\n states = demo["states"][:]
\n absolute_actions = demo["actions"][:]
\n model_xml = demo.attrs["model_file"]
\n env_info = json.loads(file["data"].attrs["env_info"])<\/p>\n

# \u517c\u5bb9\u65e7 HDF5 \u4e2d\u7f3a\u5931 compiler \u5c5e\u6027\u7684 XML；\u539f\u59cb\u6570\u636e\u53ea\u8bfb，\u4e0d\u6539\u5199\u3002
\nmodel_xml = fix_piper_xml(model_xml)
\nenv_info["physics_xml_version"] = PHYSICS_XML_VERSION<\/p>\n

count = len(states)
\nfps = int(env_info["control_freq"])<\/p>\n

assert count > 0
\nassert absolute_actions.shape == (count, 7)
\nassert np.isfinite(states).all()
\nassert np.isfinite(absolute_actions).all()
\nassert np.all(np.abs(absolute_actions[:, 6]) <= 1)
\nassert env_info["action_representation"] == (
\n "world_xyz_m + world_axis_angle_rad + gripper"
\n)<\/p>\n

# \u5b8c\u6574\u4eff\u771f\u72b6\u6001\u7684\u7b2c 0 \u7ef4\u662f\u65f6\u95f4
\nassert np.allclose(
\n np.diff(states[:, 0]),
\n 1 \/ fps,
\n atol=1e-6,
\n rtol=0,
\n)<\/p>\n

# \u56fa\u5b9a\u539f\u59cb\u89c2\u6d4b\u7ea6\u5b9a，\u907f\u514d\u6362\u673a\u5668\u540e\u56fe\u50cf\u88ab\u91cd\u590d\u7ffb\u8f6c
\nassert macros.IMAGE_CONVENTION == "opengl", (
\n "\u5f53\u524d\u56fe\u50cf\u7ea6\u5b9a\u4e0d\u662f opengl，\u8bf7\u5148\u6838\u5bf9\u56fe\u50cf\u5904\u7406\u65b9\u5f0f"
\n)<\/p>\n

# \u4e0d\u8986\u76d6\u65e7\u8f6c\u6362\u7ed3\u679c
\nOUTPUT.mkdir(parents=True, exist_ok=False)<\/p>\n

env = None
\nwriter = None<\/p>\n

try:
\n env = ControlEnv(
\n bddl_file_name=env_info["bddl_file"],
\n robots=env_info["robots"],
\n controller="OSC_POSE",
\n initialization_noise=None,
\n control_freq=fps,
\n has_renderer=False,
\n has_offscreen_renderer=True,
\n use_camera_obs=True,
\n camera_names=["agentview", "robot0_eye_in_hand"],
\n camera_widths=256,
\n camera_heights=256,
\n camera_depths=False,
\n ignore_done=True,
\n )<\/p>\n

env.seed(env_info["seed"])
\n env.reset()<\/p>\n

# \u5fc5\u987b\u6062\u590d\u91c7\u96c6\u65f6\u4fdd\u5b58\u7684\u6a21\u578b，\u5305\u62ec\u6700\u7ec8\u7684 90\u00b0 \u76f8\u673a\u8bbe\u7f6e
\n env.reset_from_xml_string(model_xml)<\/p>\n

controller = env.robots[0].controller
\n controller.uncoupling = False<\/p>\n

assert controller.impedance_mode == "fixed"
\n assert states.shape[1] == env.get_sim_state().size<\/p>\n

for name in [
\n "input_min", "input_max",
\n "output_min", "output_max",
\n ]:
\n assert np.allclose(
\n getattr(controller, name),
\n env_info["controller"][name],
\n ), f"\u63a7\u5236\u5668\u53c2\u6570\u53d8\u5316: {name}"<\/p>\n

camera_id = env.sim.model.camera_name2id(
\n "robot0_eye_in_hand"
\n )
\n assert np.isclose(
\n env.sim.model.cam_fovy[camera_id], 90.0
\n )<\/p>\n

writer = cv2.VideoWriter(
\n str(OUTPUT \/ "preview.mp4"),
\n cv2.VideoWriter_fourcc(*"mp4v"),
\n fps,
\n (512, 256),
\n )
\n assert writer.isOpened(), "\u65e0\u6cd5\u521b\u5efa\u9884\u89c8\u89c6\u9891"<\/p>\n

max_position_error = 0.0
\n max_rotation_error = 0.0
\n check_frames = {0, count \/\/ 2, count – 1}<\/p>\n

with h5py.File(OUTPUT \/ "episode.hdf5", "x") as file:
\n file.attrs["complete"] = False
\n file.attrs["source"] = str(SOURCE)
\n file.attrs["task"] = env_info["task"]
\n file.attrs["fps"] = fps
\n file.attrs["env_info"] = json.dumps(
\n env_info, ensure_ascii=False
\n )
\n file.attrs["action_representation"] = (
\n "relative controller input + gripper"
\n )
\n file.attrs["image_transform"] = (
\n "raw observation flipped in height and width"
\n )<\/p>\n

state_data = file.create_dataset(
\n "observation.state",
\n shape=(count, 8),
\n dtype="float32",
\n )
\n action_data = file.create_dataset(
\n "action",
\n shape=(count, 7),
\n dtype="float32",
\n )
\n file.create_dataset(
\n "source_frame_index",
\n data=np.arange(count),
\n )<\/p>\n

image_data = {
\n key: file.create_dataset(
\n key,
\n shape=(count, 256, 256, 3),
\n dtype="uint8",
\n chunks=(1, 256, 256, 3),
\n compression="lzf",
\n )
\n for key in CAMERAS.values()
\n }<\/p>\n

for index, (sim_state, absolute_action) in enumerate(
\n zip(states, absolute_actions)
\n ):
\n # \u6062\u590d\u52a8\u4f5c\u6267\u884c\u524d\u7684\u540c\u4e00\u5e27\u72b6\u6001；\u4e0d\u4f1a\u63a8\u8fdb\u7269\u7406\u65f6\u95f4
\n observation = env.regenerate_obs_from_state(
\n sim_state
\n )
\n controller.update(force=True)<\/p>\n

assert np.isclose(
\n env.sim.data.time, sim_state[0]
\n )<\/p>\n

# 8 \u7ef4 state：\u672b\u7aef\u4f4d\u7f6e3 + \u671d\u5411\u8f74\u89d23 + \u4e24\u6307\u4f4d\u7f6e2
\n state = np.concatenate([
\n observation["robot0_eef_pos"],
\n T.quat2axisangle(
\n observation["robot0_eef_quat"].copy()
\n ),
\n observation["robot0_gripper_qpos"],
\n ]).astype(np.float32)<\/p>\n

assert state.shape == (8,)
\n assert np.isfinite(state).all()<\/p>\n

relative_action = absolute_to_relative(
\n absolute_action, controller
\n ).astype(np.float32)
\n assert np.isfinite(relative_action).all()<\/p>\n

# \u68c0\u67e5\u4e24\u79cd\u52a8\u4f5c\u8f93\u5165\u662f\u5426\u6307\u5411\u540c\u4e00\u4e2a\u63a7\u5236\u76ee\u6807
\n controller.use_delta = False
\n controller.reset_goal()
\n controller.set_goal(absolute_action[:6])
\n absolute_goal_pos = controller.goal_pos.copy()
\n absolute_goal_ori = controller.goal_ori.copy()<\/p>\n

controller.use_delta = True
\n controller.reset_goal()
\n controller.set_goal(relative_action[:6])<\/p>\n

position_error = np.linalg.norm(
\n controller.goal_pos – absolute_goal_pos
\n )
\n rotation_error = np.linalg.norm(
\n controller.goal_ori – absolute_goal_ori
\n )<\/p>\n

assert position_error < 1e-5, (
\n f"\u5e27={index}, \u76ee\u6807\u4f4d\u7f6e\u8bef\u5dee={position_error}"
\n )
\n assert rotation_error < 1e-5, (
\n f"\u5e27={index}, \u76ee\u6807\u65cb\u8f6c\u77e9\u9635\u8bef\u5dee={rotation_error}, "
\n f"\u76f8\u5bf9\u65cb\u8f6c\u8f93\u5165={relative_action[3:6]}"
\n )<\/p>\n

max_position_error = max(
\n max_position_error, position_error
\n )
\n max_rotation_error = max(
\n max_rotation_error, rotation_error
\n )<\/p>\n

state_data[index] = state
\n action_data[index] = relative_action.astype(
\n np.float32
\n )<\/p>\n

frames = []
\n for raw_key, dataset_key in CAMERAS.items():
\n raw = observation[raw_key]
\n assert raw.shape == (256, 256, 3)
\n assert raw.dtype == np.uint8<\/p>\n

# \u4e0e\u5728\u7ebf LIBERO \u89c2\u6d4b\u5904\u7406\u4fdd\u6301\u4e00\u81f4
\n rgb = np.ascontiguousarray(
\n raw[::-1, ::-1]
\n )
\n image_data[dataset_key][index] = rgb
\n frames.append(rgb)<\/p>\n

if index in check_frames:
\n assert cv2.imwrite(
\n str(
\n OUTPUT
\n \/ f"{index:06d}_{raw_key}.png"
\n ),
\n cv2.cvtColor(
\n rgb, cv2.COLOR_RGB2BGR
\n ),
\n )<\/p>\n

preview = np.concatenate(frames, axis=1)
\n writer.write(
\n cv2.cvtColor(
\n preview, cv2.COLOR_RGB2BGR
\n )
\n )<\/p>\n

if (index + 1) % 50 == 0:
\n print(f"\u5df2\u6062\u590d: {index + 1}\/{count}")<\/p>\n

file.attrs["complete"] = True<\/p>\n

writer.release()
\n writer = None<\/p>\n

# \u4fdd\u5b58\u540e\u8bfb\u56de\u68c0\u67e5
\n with h5py.File(OUTPUT \/ "episode.hdf5", "r") as file:
\n assert bool(file.attrs["complete"])
\n assert file["observation.state"].shape == (count, 8)
\n assert file["action"].shape == (count, 7)<\/p>\n

for key in CAMERAS.values():
\n assert file[key].shape == (
\n count, 256, 256, 3
\n )<\/p>\n

elapsed = time.perf_counter() – start<\/p>\n

print("\\\\n=== A3 \u7b2c\u4e00\u6bb5\u7ed3\u679c ===")
\n print("\u539f\u59cb\u5e27\u6570:", count)
\n print("observation.state:", (count, 8))
\n print("action:", (count, 7))
\n print("\u4e24\u8def\u56fe\u50cf\u5206\u522b\u4e3a:", (count, 256, 256, 3))
\n print("\u6700\u5927\u76ee\u6807\u4f4d\u7f6e\u8bef\u5dee \/ m:", max_position_error)
\n print("\u6700\u5927\u76ee\u6807\u65cb\u8f6c\u77e9\u9635\u8bef\u5dee:", max_rotation_error)
\n print("\u6062\u590d\u4e0e\u5bfc\u51fa\u8017\u65f6 \/ s:", elapsed)
\n print("\u8f93\u51fa\u76ee\u5f55:", OUTPUT)
\n print("A3_REPLAY_EXPORT_OK")<\/p>\n

finally:
\n if writer is not None:
\n writer.release()
\n if env is not None:
\n env.close()<\/p>\n

\u8fd9\u4e00\u6b65\u505a\u4e86\u521a\u521a\u63d0\u5230\u7684\u6570\u503c\u500d\u6570\u8f6c\u6362，\u5f97\u5230\u4e86\u5e26\u6709\u56fe\u50cf\u4fe1\u606f\u3001state\u3001action\u7684hdf5.<\/p>\n

4.3.2. \u4e2d\u95f4hdf5\u8f6cLeRobotDatasetv3\u683c\u5f0f<\/h5>\n

\u8fdb\u884c\u8bad\u7ec3\u7684\u6570\u636e\u8fd8\u8981\u8fdb\u4e00\u6b65\u5c01\u88c5\u4e00\u4e0b，\u6bd4\u5982\u9700\u8981\u9610\u8ff0\u6e05\u695a\u8fd9\u4e2a\u6570\u636e\u5bf9\u5e94\u54ea\u4e2a\u4efb\u52a1\u6587\u672c\u7b49\u7b49：<\/p>\n

import time
\nfrom pathlib import Path<\/p>\n

import h5py
\nimport numpy as np<\/p>\n

from lerobot.configs.video import RGBEncoderConfig
\nfrom lerobot.datasets.lerobot_dataset import LeRobotDataset<\/p>\n

SOURCE = Path(
\n "\/root\/autodl-tmp\/workspace\/datasets\/week5\/"
\n "a3_replay_90deg\/episode.hdf5"
\n)<\/p>\n

ROOT = Path(
\n "\/root\/autodl-tmp\/workspace\/datasets\/week5\/"
\n "piper_black_bowl_a3_lerobot"
\n)<\/p>\n

# \u8fd9\u91cc\u53ea\u662f\u6570\u636e\u96c6\u6807\u8bc6，\u4e0d\u4f1a\u81ea\u52a8\u4e0a\u4f20 Hugging Face
\nREPO_ID = "dukiya\/piper_black_bowl_a3"<\/p>\n

CAMERA_KEYS = (
\n "observation.images.image",
\n "observation.images.image2",
\n)<\/p>\n

FEATURES = {
\n "observation.state": {
\n "dtype": "float32",
\n "shape": (8,),
\n "names": None,
\n },
\n "action": {
\n "dtype": "float32",
\n "shape": (7,),
\n "names": None,
\n },
\n}<\/p>\n

for key in CAMERA_KEYS:
\n FEATURES[key] = {
\n "dtype": "video",
\n "shape": (256, 256, 3),
\n "names": ["height", "width", "channels"],
\n }<\/p>\n

start = time.perf_counter()<\/p>\n

assert SOURCE.is_file(), SOURCE
\nassert not ROOT.exists(), (
\n f"\u8f93\u51fa\u76ee\u5f55\u5df2\u5b58\u5728，\u5148\u68c0\u67e5\u662f\u5426\u5df2\u6709\u8f6c\u6362\u7ed3\u679c：{ROOT}"
\n)<\/p>\n

with h5py.File(SOURCE, "r") as source:
\n assert bool(source.attrs["complete"])<\/p>\n

fps = int(source.attrs["fps"])
\n task = str(source.attrs["task"])
\n count = source["action"].shape[0]<\/p>\n

assert source["observation.state"].shape == (count, 8)
\n assert source["action"].shape == (count, 7)
\n assert source.attrs["action_representation"] == (
\n "relative controller input + gripper"
\n )<\/p>\n

for key in CAMERA_KEYS:
\n assert source[key].shape == (count, 256, 256, 3)<\/p>\n

# \u4f7f\u7528 CPU \u7684 H.264 \u7f16\u7801，\u4e0d\u4f9d\u8d56 GPU\u3002
\n encoder = RGBEncoderConfig(
\n vcodec="h264",
\n crf=18,
\n preset="fast",
\n video_backend="pyav",
\n )<\/p>\n

dataset = LeRobotDataset.create(
\n repo_id=REPO_ID,
\n root=ROOT,
\n robot_type="piper",
\n fps=fps,
\n features=FEATURES,
\n use_videos=True,
\n video_backend="pyav",
\n rgb_encoder=encoder,
\n image_writer_processes=0,
\n image_writer_threads=0,
\n encoder_threads=1,
\n )<\/p>\n

try:
\n for index in range(count):
\n frame = {
\n "observation.state": source[
\n "observation.state"
\n ][index],
\n "action": source["action"][index],
\n "task": task,
\n }<\/p>\n

for key in CAMERA_KEYS:
\n frame[key] = source[key][index]<\/p>\n

assert np.isfinite(
\n frame["observation.state"]
\n ).all()
\n assert np.isfinite(frame["action"]).all()<\/p>\n

dataset.add_frame(frame)<\/p>\n

if (index + 1) % 50 == 0:
\n print(f"\u5df2\u5199\u5165: {index + 1}\/{count}")<\/p>\n

# \u4e24\u8def\u76f8\u673a\u4e32\u884c\u7f16\u7801，\u907f\u514d\u989d\u5916\u542f\u52a8\u7f16\u7801\u5b50\u8fdb\u7a0b
\n dataset.save_episode(parallel_encoding=False)<\/p>\n

finally:
\n # \u5fc5\u987b\u8c03\u7528，\u786e\u4fdd parquet \u7b49\u6587\u4ef6\u5b8c\u6574\u843d\u76d8
\n dataset.finalize()<\/p>\n

conversion_seconds = time.perf_counter() – start<\/p>\n

# \u4ece\u6700\u7ec8\u6570\u636e\u96c6\u91cd\u65b0\u52a0\u8f7d，\u800c\u4e0d\u662f\u53ea\u68c0\u67e5\u4e2d\u95f4\u6587\u4ef6
\nchecked = LeRobotDataset(
\n repo_id=REPO_ID,
\n root=ROOT,
\n video_backend="pyav",
\n)<\/p>\n

assert len(checked) == count
\nassert checked.meta.total_episodes == 1
\nassert checked.meta.info["codebase_version"] == "v3.0"<\/p>\n

# \u62bd\u67e5\u9996\u3001\u4e2d\u3001\u672b\u5e27，\u68c0\u67e5\u4f4e\u7ef4\u6570\u636e\u4e0e\u539f\u6587\u4ef6\u4e00\u81f4，
\n# \u5e76\u786e\u8ba4\u4e24\u8def\u89c6\u9891\u53ef\u4ee5\u89e3\u7801\u6210\u6a21\u578b\u8bfb\u53d6\u7684\u56fe\u50cf\u5f20\u91cf\u3002
\nwith h5py.File(SOURCE, "r") as source:
\n for index in (0, count \/\/ 2, count – 1):
\n sample = checked[index]<\/p>\n

for key in ("observation.state", "action"):
\n np.testing.assert_allclose(
\n sample[key].numpy(),
\n source[key][index],
\n rtol=0,
\n atol=1e-7,
\n )<\/p>\n

for key in CAMERA_KEYS:
\n image = sample[key].numpy()
\n assert image.shape == (3, 256, 256)
\n assert np.isfinite(image).all()
\n assert image.min() >= 0
\n assert image.max() <= 1<\/p>\n

print("\u8bfb\u56de\u68c0\u67e5\u901a\u8fc7，\u5e27:", index)<\/p>\n

print("\\\\n=== A3 LeRobot \u6570\u636e\u96c6\u7ed3\u679c ===")
\nprint("\u6570\u636e\u96c6\u683c\u5f0f:", checked.meta.info["codebase_version"])
\nprint("episodes:", checked.meta.total_episodes)
\nprint("\u603b\u5e27\u6570:", len(checked))
\nprint("fps:", checked.meta.fps)
\nprint("\u4efb\u52a1:", task)<\/p>\n

for key in (
\n "observation.state",
\n "action",
\n *CAMERA_KEYS,
\n):
\n feature = checked.meta.features[key]
\n print(
\n f"{key}: dtype={feature['dtype']}, "
\n f"shape={feature['shape']}"
\n )<\/p>\n

print("\u672c\u9636\u6bb5\u8f6c\u6362\u8017\u65f6 \/ s:", conversion_seconds)
\nprint("\u8f93\u51fa\u76ee\u5f55:", ROOT)
\nprint("A3_LEROBOT_WRITE_OK")<\/p>\n

\u505a\u4e00\u4e2a\u7b80\u5355\u7684\u53ef\u89c6\u5316\u9a8c\u8bc1：<\/p>\n

source \/root\/miniconda3\/etc\/profile.d\/conda.sh
\nconda activate lerobot<\/p>\n

export HF_HOME=\/root\/autodl-tmp\/huggingface
\nexport HF_HUB_OFFLINE=1
\nexport OMP_NUM_THREADS=1
\nexport MKL_NUM_THREADS=1
\nexport OPENBLAS_NUM_THREADS=1<\/p>\n

lerobot-dataset-viz \\\\
\n –repo-id dukiya\/piper_black_bowl_a3 \\\\
\n –root \/root\/autodl-tmp\/workspace\/datasets\/week5\/piper_black_bowl_a3_lerobot \\\\
\n –episode-index 0 \\\\
\n –batch-size 1 \\\\
\n –num-workers 0 \\\\
\n –display-compressed-images \\\\
\n –save 1 \\\\
\n –output-dir \/root\/autodl-tmp\/workspace\/my_test\/week5\/a3_viz<\/p>\n

\"\"<\/p>\n

\u8bc1\u660e\u6570\u636e\u8f6c\u6362\u6210\u529f，\u80fd\u88ablerobot\u8bc6\u522b\u3002<\/p>\n

\n

\u603b\u7ed3<\/h3>\n

\u672c\u6587\u4ecb\u7ecd\u4e86\u952e\u76d8\u9065\u64cd\u4f5c\u91c7\u96c6piper\u9ed1\u7897\u4efb\u52a1\u6570\u636e\u96c6，\u8bbe\u8ba1\u9065\u64cd\u4f5c\u7684\u5b9e\u73b0\u548c\u6570\u636e\u8f6c\u6362\u3002<\/p>\n

\u6587\u7ae0\u4ec5\u505a\u5b66\u4e60\u5206\u4eab，\u5982\u6709\u8c2c\u8bef，\u6b22\u8fce\u6307\u6b63\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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