让机器人在可变形地面上自然行走，靠的是更真实的物理模拟与自适应控制。
MILD: Tractable Terrain Modeling for Learning Improved Bipedal Locomotion on Deformable Surfaces
- 用离散元接触求解器模拟脚与地面的异质交互。
- 训练出的控制器在真实可变形地面上表现更自然、适应性更强。
- 适合做机器人行走控制或仿真系统研发的工程师参考。
让机器人在可变形地面上行走对灾难救援和行星探测等应用至关重要。尽管双足机器人潜力巨大，但现有模拟器无法捕捉此类表面在时空上的异质性，限制了其运动能力。本文提出MILD，采用基于物理的离散元接触求解器，精确模拟脚与地面的空间差异性相互作用。结合该模型，通过深度强化学习与潜在变量调制、本体感知估计训练地形感知型步态控制器。定量对比显示，该方法在训练中生成更丰富多样的接触场景，使控制器在真实可变形地面上表现出自然适应性。硬件实验验证了系统在多种表面刚度下具备在线地形识别与适应能力。
原文摘要 · Abstract (English)
Enabling robots to walk on yielding terrain is vital for applications ranging from disaster response to planetary exploration. While bipedal robots hold immense potential, their locomotion on deformable surfaces remains limited as current simulators fail to capture the spatiotemporal heterogeneity of such yielding substrates. We present MILD, featuring a physics-grounded discrete-element contact solver that accurately simulates spatially varying foot-terrain interactions. Complementing this model, we train a terrain-aware locomotion controller via deep reinforcement learning with latent modulation and proprioceptive estimation. Quantitative comparisons against state-of-the-art methods show our approach generates more diverse and realistic contact scenarios during training, resulting in controllers that exhibit natural adaptation on real deformable surfaces. Through hardware experiments, we demonstrate the system's capability for online terrain identification and adaptation across a wide range of surface stiffness.
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