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ROVE framework improves humanoid manipulation with imperfect human interventions

Researchers have introduced ROVE, a reinforcement learning framework designed to improve humanoid manipulation by effectively utilizing imperfect human interventions. The system addresses challenges in collecting high-quality intervention data by employing Optimistic Value Estimation (OVE) to prioritize valuable actions from mixed-quality trajectories. ROVE also incorporates cross-embodiment human experience videos to enhance supervision for failure and recovery modes, ultimately outperforming existing baselines on complex manipulation tasks. AI

IMPACT Enhances humanoid robot capabilities by improving learning from human feedback, potentially accelerating real-world applications.

RANK_REASON The cluster contains a research paper detailing a new framework for AI, specifically for robotics and reinforcement learning.

Read on arXiv cs.LG →

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Wei Xiao, Weiliang Tang, Yuying Ge, Hui Zhou, Yao Mu, Li Zhang, Yixiao Ge ·

    ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning

    arXiv:2606.17011v1 Announce Type: cross Abstract: 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…

  2. arXiv cs.LG TIER_1 English(EN) · Yixiao Ge ·

    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 col…