Tingxuan Leng | Searcharxiv

arXiv2026

Preference-Conditioned Multi-Objective RL for Integrated Command Tracking and Force Compliance in Humanoid Locomotion

Humanoid locomotion requires not only accurate command tracking for navigation but also compliant responses to external forces during human interaction. Despite significant progress, existing RL approaches mainly emphasize robustness, yielding policies that resist external forces but lack compliance particularly challenging for inherently unstable humanoids. In this work, we address this by formulating humanoid locomotion as a multi-objective optimization problem that balances command tracking and external force compliance. We introduce a preference-conditioned multi-objective RL (MORL) framework that enables a single omnidirectional locomotion policy to trade off between command following and force compliance via a user-specified preference input. External forces are modeled via velocity-resistance factor for consistent reward design, and training leverages an encoder-decoder structure that infers task-relevant privileged features from deployable observations. We validate our approach in both simulation and real-world experiments on a humanoid robot. Experimental results in simulation and on hardware show that the framework trains stably and enables deployable preference-conditioned humanoid locomotion.

cs.RO↗

arXiv2026

APEX: Learning Adaptive High-Platform Traversal for Humanoid Robots

Humanoid locomotion has advanced rapidly with deep reinforcement learning (DRL), enabling robust feet-based traversal over uneven terrain. Yet platforms beyond leg length remain largely out of reach because current RL training paradigms often converge to jumping-like solutions that are high-impact, torque-limited, and unsafe for real-world deployment. To address this gap, we propose APEX, a system for perceptive, climbing-based high-platform traversal that composes terrain-conditioned behaviors: climb-up and climb-down at vertical edges, walking or crawling on the platform, and stand-up and lie-down for posture reconfiguration. Central to our approach is a generalized ratchet progress reward for learning contact-rich, goal-reaching maneuvers. It tracks the best-so-far task progress and penalizes non-improving steps, providing dense yet velocity-free supervision that enables efficient exploration under strong safety regularization. Based on this formulation, we train LiDAR-based full-body maneuver policies and reduce the sim-to-real perception gap through a dual strategy: modeling mapping artifacts during training and applying filtering and inpainting to elevation maps during deployment. Finally, we distill all six skills into a single policy that autonomously selects behaviors and transitions based on local geometry and commands. Experiments on a 29-DoF Unitree G1 humanoid demonstrate zero-shot sim-to-real traversal of 0.8 meter platforms (approximately 114% of leg length), with robust adaptation to platform height and initial pose, as well as smooth and stable multi-skill transitions.

cs.RO↗