歩みに注意：正確な人型ロボットの足場追跡のための汎用学習フレームワーク
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
人型ロボットの足場配置を明示的に追跡する汎用の強化学習フレームワークを提案し、実世界のノイズに頑健な目標表現とゴールサンプラーにより、様々な高レベルプランナーと組み合わせ可能な低レベルコントローラを実現した。
詳しい要約
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著者: Alessandro Montenegro, Shihao Li, Puze Liu, Alberto Maria Metelli, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately. While reinforcement learning with velocity-commanded policies has achieved remarkable robustness in humanoid locomotion, this approach lacks explicit control of the foothold placement, leading to unsafe behavior, such as stepping onto human feet, or imprecise navigation, hindering the following manipulation task. Conversely, explicit foothold-tracking policies offer a promising alternative by directly being commanded with target foot poses. However, existing approaches are often limited by unrealistic state assumptions, compromising real-world deployment, or they are part of staged pipelines, making them tied to specific downstream tasks. In this work, we introduce a novel, lightweight framework for training general-purpose 3D foothold-tracking policies. By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains. Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation. Designed for direct real-world transfer, our policy acts as a standalone low-level controller that can be seamlessly paired with various high-level foothold generators. We demonstrate the effectiveness of our framework through extensive experiments in simulation and in the real world. By coupling our policy with different upstream planners, we achieve natural and accurate locomotion in challenging settings, paving the way for loco-manipulation tasks in complex environments.
