FR-Net: 質量接触予測による難地形でのロバストな四脚ロボットの転倒復帰学習
FR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction
限られたセンサ入力から質量分布と接触状態を予測するネットワークを中核に、シミュレーションでの特権学習を通じて、様々な地形で任意の転倒姿勢から四脚ロボットが安全に復帰する方策を学習するフレームワークを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yidan Lu, Yinzhao Dong, Jiahui Zhang, Ji Ma, Peng Lu
分類: cs.RO
原文アブストラクト
Fall recovery for legged robots remains challenging, particularly on complex terrains where traditional controllers fail due to incomplete terrain perception and uncertain interactions. We present \textbf{FR-Net}, a learning-based framework that enables quadrupedal robots to recover from arbitrary fall poses across diverse environments. Central to our approach is a Mass-Contact Predictor network that estimates the robot's mass distribution and contact states from limited sensory inputs, facilitating effective recovery strategies. Our carefully designed reward functions ensure safe recovery even on steep stairs without dangerous rolling motions common to existing methods. Trained entirely in simulation using privileged learning, our framework guides policy learning without requiring explicit terrain data during deployment. We demonstrate the generalization capabilities of \textbf{FR-Net} across different quadrupedal platforms in simulation and validate its performance through extensive real-world experiments on the Go2 robot in 10 challenging scenarios. Our results indicate that explicit mass-contact prediction is key to robust fall recovery, offering a promising direction for generalizable quadrupedal skills.
