DexHiL: 一种用于灵巧操作中视觉-语言-动作模型后训练的人机协同框架
DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation
AI总结：
DexHiL是一种用于灵巧操作中视觉-语言-动作模型后训练的人机协同框架，通过干预意识的数据采样策略和轻量级远程操作界面，显著提升了模型在不同任务中的成功率。
摘要：
尽管视觉-语言-动作（VLA）模型在机器人操作中展示了令人鼓舞的泛化能力，但将其部署到特定且复杂的下游任务仍需要有效的后训练方法。同时，人机协同（HiL）学习已被证明是改进机器人策略的强大机制。然而，将这一范式扩展到灵巧操作仍具挑战性：多指控制是高维的、接触密集的，并且执行分布与标准臂运动有显著差异，使现有的灵巧VLA系统在可靠性和适应性上受限。我们提出了DexHiL，这是首个集成臂-手的人机协同框架，用于灵巧VLA模型，使臂和灵巧手能够在单一系统内协调干预。DexHiL引入了一种干预意识的数据采样策略，优先选择用于后训练的修正段，同时配备一个轻量级的远程操作界面，支持执行过程中的即时人工修正。实车实验表明，DexHiL作为一种有效的后训练框架，实现了显著的性能提升，在不同任务中，其成功率平均比标准离线-only微调基线高出25%。
英文摘要：
While Vision-Language-Action (VLA) models have demonstrated promising generalization capabilities in robotic manipulation, deploying them on specific and complex downstream tasks still demands effective post-training. In parallel, Human-in-the-Loop (HiL) learning has proven to be a powerful mechanism for refining robot policies. However, extending this paradigm to dexterous manipulation remains challenging: multi-finger control is high-dimensional, contact-intensive, and exhibits execution distributions that differ markedly from standard arm motions, leaving existing dexterous VLA systems limited in reliability and adaptability. We present DexHiL, the first integrated arm-hand human-in-the-loop framework for dexterous VLA models, enabling coordinated interventions over the arm and the dexterous hand within a single system. DexHiL introduces an intervention-aware data sampling strategy that prioritizes corrective segments for post-training, alongside a lightweight teleoperation interface that supports instantaneous human corrections during execution. Real-robot experiments demonstrate that DexHiL serves as an effective post-training framework, yielding a substantial performance leap, outperforming standard offline-only fine-tuning baselines by an average of 25% in success rates across distinct tasks. Project page: https://chenzhongxi-sjtu.github.io/dexhil/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
