ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
Human interventions provide crucial corrective signals for post-training Vision-Language-Action (VLA) models. However, enabling seamless humanoid interventions is a formidable systems challenge due to complex whole-body kinematics and dexterous-hand control. Consequently, the collected intervention trajectories are often suboptimal, and methods that rely on human interventions as expert supervision can absorb hesitant, inefficient, or even erroneous behaviors. To address both the system and algorithmic challenges, we propose ROVE, a reinforcement learning framework for humanoid VLA post-training with imperfect human interventions. First, ROVE introduces a human-in-the-loop pipeline capabl...
| Authors | Wei Xiao, Weiliang Tang, Yuying Ge et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 June 2026 |
| Last updated at source | 2 October 2026 |
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