发表机构
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology; School of Physics, Georgia Institute of Technology; Northeastern University（佐治亚理工学院机器人与智能机器研究所; 佐治亚理工学院物理学院; 东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对颗粒地形上人形机器人运动难题，提出基于3D RFT的物理接触模型和教师-学生强化学习控制器，实现地形自适应，仿真和硬件实验验证了高成功率和零样本适应能力。
AI 中文摘要
由于足部与地形相互作用的动力学复杂且难以建模，人形机器人在颗粒地形上的运动仍然是一个重大挑战。现有方法要么忽略颗粒接触动力学，要么采用带有启发式切向分量的简化法向力模型。在这项工作中，我们提出了一种基于三维阻力理论（3D RFT）的物理基础颗粒接触模型，并高效模拟颗粒地形用于强化学习（RL）训练。与传统的刚性接触模型和带有临时启发式规则的简化颗粒接触模型不同，我们的接触求解器无需借助启发式规则即可产生物理上准确的颗粒侵入动力学。它在训练过程中捕捉真实的穿透和切向阻力，使策略能够学习到可可靠迁移到真实颗粒地形的行为，而刚性接触模型在这些地形上会失效。为了适应不同的地形条件，我们通过教师-学生强化学习训练了一个地形自适应运动控制器，使用变分自编码器将地形信息编码为紧凑的潜在表示。使用材料点法（MPM）和NVIDIA Newton的仿真研究表明，我们的方法能够泛化到未见过的颗粒地形，比基线方法实现了显著更高的成功率，并展示了零样本地形识别和适应能力。我们进一步通过在不同真实颗粒地形（包括玄武岩、干沙和海沙）上的广泛硬件实验验证了我们的方法。据我们所知，这是首次在真实颗粒地形上演示敏捷的人形机器人运动。项目页面：此https URL
英文摘要
Humanoid locomotion on granular terrain remains a significant challenge due to its complex foot-terrain interaction dynamics that are difficult to model. Existing approaches either ignore granular contact dynamics or incorporate simplified normal force models with heuristic tangential components. In this work, we present a physics-grounded granular contact model based on three-dimensional resistive force theory (3D RFT) and efficiently simulate granular terrain for reinforcement learning (RL) training. Unlike traditional rigid contact models and simplified granular contact models with ad-hoc heuristics, our contact solver produces physically accurate granular intrusion dynamics without resorting to heuristics. It captures realistic penetration and tangential drag during training, enabling the policy to learn behaviors that transfer reliably to real-world granular terrain where rigid contact models fail. To adapt to varying terrain conditions, we train a terrain-adaptive locomotion controller via teacher-student RL, using a variational autoencoder to encode terrain information into a compact latent representation. Simulation studies using material point method (MPM) with NVIDIA Newton demonstrate that our method generalizes to unseen granular terrains, achieves a significantly higher success rate than baselines, and demonstrates zero-shot terrain identification and adaptation. We further validate our approach through extensive hardware experiments across diverse real-world granular terrains including basalt, dry sand, and beach sand. To the best of our knowledge, this is the first demonstration of agile humanoid locomotion on real-world granular terrain. Project page: https://humanoid-gm-locomotion.github.io/HUMANOID-GM/
