MILD: Tractable Terrain Modeling for Learning Improved Bipedal Locomotion on Deformable Surfaces

2026-08-20

AI Summary

研究解决了机器人在可变形地面上行走的问题，通过MILD模型模拟地面互动，并使用深度强化学习训练控制器，实现对不同硬度表面的适应。

Abstract

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.

Problem

• bipedal locomotion
• deformable surfaces
• spatiotemporal heterogeneity
• simulators

Innovation

• MILD
• discrete-element contact solver
• deep reinforcement learning
• terrain-aware locomotion controller
• deformable surfaces