HumDex: ヒューマノイド器用操作を簡単にする
HumDex: Humanoid Dexterous Manipulation Made Easy
ヒューマノイド全身の器用な操作のためのポータブル遠隔操作システムを提案し、IMUベースのモーショントラッキングと学習ベースのリターゲティングで高品質なデモデータ収集を実現。さらに、多様な人間の動作データでの事前学習とロボットデータでの微調整による模倣学習フレームワークを導入し、汎化性能を向上させた。
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著者: Liang Heng, Yihe Tang, Jiajun Xu, Henghui Bao, Di Huang, Yue Wang
分類: cs.RO
原文アブストラクト
This paper investigates humanoid whole-body dexterous manipulation, where the efficient collection of high-quality demonstration data remains a central bottleneck. Existing teleoperation systems often suffer from limited portability, occlusion, or insufficient precision, which hinders their applicability to complex whole-body tasks. To address these challenges, we introduce HumDex, a portable teleoperation system designed for humanoid whole-body dexterous manipulation. Our system leverages IMU-based motion tracking to address the portability-precision trade-off, enabling accurate full-body tracking while remaining easy to deploy. For dexterous hand control, we further introduce a learning-based retargeting method that generates smooth and natural hand motions without manual parameter tuning. Beyond teleoperation, HumDex enables efficient collection of human motion data. Building on this capability, we propose a two-stage imitation learning framework that first pre-trains on diverse human motion data to learn generalizable priors, and then fine-tunes on robot data to bridge the embodiment gap for precise execution. We demonstrate that this approach significantly improves generalization to new configurations, objects, and backgrounds with minimal data acquisition costs. The entire system is fully reproducible and open-sourced at https://github.com/physical-superintelligence-lab/humdex.
