全身ヒューマノイド制御のための拡散ポリシー訓練におけるドメインランダム化の役割
The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control
ヒューマノイドの全身制御に拡散ポリシーを適用し、ドメインランダム化で生成した模倣データの多様性と量が性能に与える影響をIsaacGym上で調査した。歩行には操作タスクより大規模で多様なデータが必要だと示した。
著者: Oleg Kaidanov, Firas Al-Hafez, Yusuf Suvari, Boris Belousov, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
Humanoids have the potential to be the ideal embodiment in environments designed for humans. Thanks to the structural similarity to the human body, they benefit from rich sources of demonstration data, e.g., collected via teleoperation, motion capture, or even using videos of humans performing tasks. However, distilling a policy from demonstrations is still a challenging problem. While Diffusion Policies (DPs) have shown impressive results in robotic manipulation, their applicability to locomotion and humanoid control remains underexplored. In this paper, we investigate how dataset diversity and size affect the performance of DPs for humanoid whole-body control. In a simulated IsaacGym environment, we generate synthetic demonstrations by training Adversarial Motion Prior (AMP) agents under various Domain Randomization (DR) conditions, and we compare DPs fitted to datasets of different size and diversity. Our findings show that, although DPs can achieve stable walking behavior, successful training of locomotion policies requires significantly larger and more diverse datasets compared to manipulation tasks, even in simple scenarios.
