JAEGER: 二層構造のヒューマノイド全身制御器
JAEGER: Dual-Level Humanoid Whole-Body Controller
上半身と下半身を別々の制御器に分け、粗い速度追従と細かい関節角追従の両方を可能にしたヒューマノイド全身制御を、人間モーションの再ターゲティングとカリキュラム学習で訓練した研究。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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6. 次に読むべき論文は？
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著者: Ziluo Ding, Haobin Jiang, Yuxuan Wang, Zhenguo Sun, Yu Zhang, Xiaojie Niu, Ming Yang, Weishuai Zeng, Xinrun Xu, Zongqing Lu
分類: cs.RO, cs.AI
原文アブストラクト
This paper presents JAEGER, a dual-level whole-body controller for humanoid robots that addresses the challenges of training a more robust and versatile policy. Unlike traditional single-controller approaches, JAEGER separates the control of the upper and lower bodies into two independent controllers, so that they can better focus on their distinct tasks. This separation alleviates the dimensionality curse and improves fault tolerance. JAEGER supports both root velocity tracking (coarse-grained control) and local joint angle tracking (fine-grained control), enabling versatile and stable movements. To train the controller, we utilize a human motion dataset (AMASS), retargeting human poses to humanoid poses through an efficient retargeting network, and employ a curriculum learning approach. This method performs supervised learning for initialization, followed by reinforcement learning for further exploration. We conduct our experiments on two humanoid platforms and demonstrate the superiority of our approach against state-of-the-art methods in both simulation and real environments.
