Sizhe Wei, Xulin Chen, Fengze Xie, Garrett Ethan Katz, Zhenyu Gan, Lu Gan
原文摘要
Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned with robot physics. Generic networks ignore kinematic structure, while graph-based policies encode connectivity without specifying how physical quantities transform across symmetric body parts. We introduce a morphological symmetry graph representation for locomotion policy learning, instantiated as MS-PPO with PPO on Go2 and MS-SAC with FastSAC on G1. Starting from the robot's topological graph, our representation explicitly incorporates the permutations and sign changes induced by morphological symmetry on observations and actions, yielding a graph actor with an equivariant action mean and an invariant critic. We evaluate the representation on Unitree Go2 command tracking and asymmetric joint-failure tasks, and on Unitree G1 29-DoF whole-body locomotion, including zero-shot sim-to-real deployment. Our approach achieves the lowest tracking errors in both simulation and hardware, and the highest mean simulation returns across all three joint-failure types on Go2 among the evaluated baselines. On Go2, MS-PPO uses only 34.6% of the parameters of the PPO baseline. These results support the effectiveness of jointly encoding topology and morphological symmetry across robot embodiments and learning frameworks.
Ziwon Yoon, Seok Hwan Kim, Yiyu Chen, Quan Nguyen, Lu Gan, Ye Zhao
原文摘要
Humanoid robots have achieved significant progress in locomotion and manipulation, enabling traversal over rough terrain and richer interaction with the environment, such as moving obstacles. However, most traditional navigation methods assume non-interactive environments and focus only on traversal. While Navigation Among Movable Obstacles (NAMO) approaches relax this assumption, prior efforts have largely focused on flat environments and reactive interaction, rather than predicting object movability and planning ahead. These limitations become critical in outdoor rough-terrain scenarios, where robots must jointly reason about terrain traversability and object movability. In this work, we address NAMO for humanoid robots in cluttered outdoor rough terrain by proposing a hybrid planning framework that combines semantic priors from a Vision-Language Model (VLM) with embodied robot interaction data through a Gaussian process (GP) residual model. The GP corrects the VLM-derived movability prior to produce embodiment-grounded probabilistic movability estimates, which are then incorporated into hierarchical uncertainty-aware planning. The proposed approach allows humanoids to jointly leverage locomotion and manipulation for efficient and safe navigation, representing the first solution to NAMO in rough terrain through predictive movability reasoning and probabilistic VLM integration.