自己衝突回避を考慮した狭所移動ヒューマノイドの全身動作計画
Whole-Body Planning for Humanoids Navigating Confined Spaces via Self-Collision Avoidance References
狭い環境でのヒューマノイド移動のため、到達可能な剛体ボリューム上で経路計画を行い、衝突回避を組み込んだ3段階の全身計画フレームワークを提案。生成した計画を残差強化学習で追従し、Unitree G1で検証した。
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著者: Carlos Gonzalez, Luis Sentis
分類: cs.RO
原文アブストラクト
Humanoid locomotion in highly confined environments requires navigating dense environmental obstacles and complex self-collision bounds while maintaining multi-contact dynamic feasibility. Traditional trajectory optimizers frequently struggle in these restricted spaces, as navigating the large collision space with splines on particle abstractions is insufficient and leads to poor local minima. To address this, we propose a three-stage whole-body planning framework that formulates kinematic path planning directly over kinematically reachable rigid-body volumes. By integrating differentiable collision avoidance into a reachability-constrained formulation, our framework synthesizes volume-informed guides that reliably guide a full-order trajectory optimizer over long horizons. We show that these optimized plans serve as high-quality references to train a residual reinforcement learning policy for robust online execution. We validate our approach on the Unitree G1 humanoid across three benchmark testbeds exceeding NIST emergency response standards, achieving restricted confinement ratios ($C_r < 1.5$). Our framework generates feasible trajectories across 12-to-18-second tasks with complex foot and hand contacts where standard baselines fail, while the learned policy successfully tracks these plans under extensive domain randomization in physics simulation.