通过预测质量与接触状态，让四足机器人在复杂地形上自动翻身后恢复平衡。
FR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction
- 用神经网络预测机器人的质量分布和接触点，辅助决策。
- 在10种真实挑战场景中成功恢复，避免危险翻滚动作。
- 无需部署时的地形数据，适合跨平台应用。
四足机器人在复杂地形上的跌倒恢复仍具挑战性，传统控制器因地形感知不全和交互不确定性而失效。本文提出FR-Net，一种基于学习的框架，使四足机器人可在多种环境中从任意跌倒姿态实现有效恢复。核心是质量-接触预测网络（Mass-Contact Predictor），仅凭有限传感输入即可估计机器人的质量分布与接触状态，从而生成有效恢复策略。设计的奖励函数确保在陡峭楼梯等场景下安全恢复，避免现有方法常见的危险滚动行为。整个框架在仿真中通过特权学习训练，部署时不依赖显式地形数据。我们在仿真中验证了其在不同四足平台上的泛化能力，并在Go2机器人上完成了10种挑战场景的实地测试。结果表明，显式预测质量与接触状态是实现鲁棒跌倒恢复的关键，为可泛化的四足技能提供了新方向。
原文摘要 · Abstract (English)
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.
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