SkateNav: 受動車輪四脚ロボットの適応スケーティングによる省エネナビゲーション学習
SkateNav: Learning Energy-Efficient Navigation through Adaptive Skating on Passive-Wheel Quadrupedal Robots
受動車輪四脚ロボットが脚で蹴って滑走するスケーティング動作を強化学習で獲得し、時間予算付き目標到達目的と適応的スケーティング周期により操舵・推進・タイミングを統合的に学習するナビゲーション手法を提案した。
著者: Jeil Jeong, Taeyun Kim, Sangjin Kim, Jinyoung Choi, Sung-Eui Yoon
分類: cs.RO
原文アブストラクト
Passive-wheel quadrupedal robots offer energy-efficient locomotion through leg-driven propulsion and momentum-driven gliding. However, navigation requires coordinating steering with intermittent propulsion and adapting push-glide timing to task demands. Velocity-tracking objectives may suppress the speed variations inherent in skating, discouraging efficient gliding. To address these challenges, we propose SkateNav, a unified skating-navigation framework based on reinforcement learning. A time-budgeted goal-reaching objective allows the policy to jointly learn steering, propulsion, and their temporal allocation. An adaptive skating cycle structures gliding, pushing, and recovery, while frequency modulation at every control step adjusts phase durations to navigation demands. Simulation experiments demonstrate higher average success rates and lower cost of transport than the evaluated planner-controller baselines, with ablations confirming the contributions of cycle structure and frequency adaptation. Real-world experiments demonstrate zero-shot transfer from simulation to a passive-wheel Mini Cheetah across cluttered indoor spaces, slopes, and paved outdoor surfaces. Supplementary videos are available on the project page: https://ktw1404.github.io/SkateNav/