Robust Autonomous Humanoid Loco-Manipulation

Develop robust autonomous loco-manipulation skills for humanoid robots.

Background

The paper addresses the challenge of enabling humanoid robots to perform complex, long-horizon loco-manipulation tasks autonomously and robustly. While reinforcement learning has shown success for legged locomotion, transferring these successes to manipulation-rich scenarios remains difficult due to planning complexity and environment interactions.

DreamControl-v2 proposes training a guided diffusion model directly in the robot’s motion space, aggregating diverse human and robot datasets to produce higher-quality reference trajectories for downstream RL. Despite these advances, the authors explicitly frame the broader goal—robust autonomous loco-manipulation for humanoids—as an open problem motivating their work.