VIP-Loco: 視覚誘導による無限地平計画フレームワークを用いた脚式移動
VIP-Loco: A Visually Guided Infinite Horizon Planning Framework for Legged Locomotion
脚式ロボットの移動において、視覚認識と強化学習を統合し、無限地平線のモデル予測制御で計画するフレームワークを提案。シミュレーションで多様な地形と複数のロボット形態で検証した。
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著者: Aditya Shirwatkar, Satyam Gupta, Shishir Kolathaya
分類: cs.RO
原文アブストラクト
Perceptive locomotion for legged robots requires anticipating and adapting to complex, dynamic environments. Model Predictive Control (MPC) serves as a strong baseline, providing interpretable motion planning with constraint enforcement, but struggles with high-dimensional perceptual inputs and rapidly changing terrain. In contrast, model-free Reinforcement Learning (RL) adapts well across visually challenging scenarios but lacks planning. To bridge this gap, we propose VIP-Loco, a framework that integrates vision-based scene understanding with RL and planning. During training, an internal model maps proprioceptive states and depth images into compact kinodynamic features used by the RL policy. At deployment, the learned models are used within an infinite-horizon MPC formulation, combining adaptability with structured planning. We validate VIP-Loco in simulation on challenging locomotion tasks, including slopes, stairs, crawling, tilting, gap jumping, and climbing, across three robot morphologies: a quadruped (Unitree Go1), a biped (Cassie), and a wheeled-biped (TronA1-W). Through ablations and comparisons with state-of-the-art methods, we show that VIP-Loco unifies planning and perception, enabling robust, interpretable locomotion in diverse environments.
