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tmeynier
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hepha_act_100_simple_drawer_5

Tasks:
Robotics
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1K - 10K
Tags:
lerobot
robotics
imitation-learning
mujoco
act
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timestamp
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3.133333
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3.233333
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tmeynier/hepha_act_100_simple_drawer_5

LeRobot-style behavior-cloning dataset generated from the Hepha MuJoCo simulation.

Summary

  • Robot type: hepha_mujoco
  • Codebase version: v3.0
  • Episodes: 100
  • Frames: 200000
  • FPS: 30
  • Joint normalization: min_max_0_1

Features

  • timestamp: float32 [1]
  • frame_index: int64 [1]
  • episode_index: int64 [1]
  • index: int64 [1]
  • task_index: int64 [1]
  • episode.drawer_index: int64 [1]
  • episode.cube_position: float32 [3]
  • episode.cube_quaternion: float32 [4]
  • observation.drawer_index: int64 [1]
  • observation.state: float32 [15]
  • action: float32 [15]
  • observation.state_raw: float32 [15]
  • action_raw: float32 [15]
  • observation.images.head_camera: video [3, 480, 640]

Policy-Facing Columns

  • observation.state: normalized robot joints in [0, 1]
  • observation.drawer_index: selected drawer target for each frame
  • action: normalized next-step robot joint targets in [0, 1]
  • observation.images.head_camera: RGB video frames from the robot camera

Extra Columns

  • observation.state_raw: raw MuJoCo joint positions
  • action_raw: raw next-step MuJoCo joint positions
  • episode.drawer_index: selected drawer index for each frame
  • episode.cube_position: initial cube position for each frame
  • episode.cube_quaternion: initial cube orientation for each frame

Notes

The dataset was produced by first computing the full IK episode, then resampling the robot joint trajectory at a constant normalized joint speed before saving frames/actions. Training code appends the normalized drawer target (drawer_index - 1) / 8 to the policy state when observation.drawer_index is present.

Joint limits and the normalization formula are stored in meta/info.json.
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Models trained or fine-tuned on tmeynier/hepha_act_100_simple_drawer_5