Title: WholeBodyVLA: Towards Unified Latent VLA for Whole-body Loco-manipulation Control
Primary area: applications to robotics, autonomy, planning
Keywords: Humanoid Robots, Vision-Language-Action Model, Locomotion and Manipulation, Whole-Body Control, Reinforcement Learning

Abstract: Humanoid robots require precise locomotion and dexterous manipulation to per-form challenging locomanipulation tasks. Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion. This confines the robot to a limited workspace, preventing it from performing large-space loco-manipulation. We attribute this to: (1) the scarcity of data capturing unified loco-manipulation behaviors, and (2) the limited precision and stability of existing reinforcement learning (RL) controllers, which arises from redundant velocity-tracking objectives and unstructured upper-body perturbations. To address these limitations, we introduce WholeBodyVLA, a unified framework for humanoid loco-manipulation. For data scarcity, we employ unified latent learning to extract manipulation-aware locomotion knowledge from low-cost video data. Moreover, an efficient data collection pipeline is devised to augment the dataset and scale the benefits. Furthermore, WholeBodyVLA is equipped with a loco–manipulation–oriented (LMO) RL policy, which replaces the continuous command interface with a discrete one and integrates structured manipulation-induced perturbations. Such a design enables the humanoid to advance, turn, and squat with high precision while maintaining stability under diverse manipulation-induced disturbances. To the best of our knowledge, WholeBodyVLA is one of its kind enabling large-space humanoid loco–manipulation. It is verified via comprehensive experiments on the AgiBot X2 humanoid, outperforming prior baseline by 21.3%. Our method demonstrates robust multi-tasking capabilities and strong generalization. Code and checkpoints would be made public.

TL;DR: We present WholeBodyVLA, a unified VLA framework enabling large-space humanoid loco-manipulation via unified latent learning and loco–manipulation–oriented RL.