DexTac: 通过手把手教学学习接触感知的视觉触觉策略
DexTac: Learning Contact-aware Visuotactile Policies via Hand-by-hand Teaching
AI总结：
DexTac通过手把手教学学习接触感知的视觉触觉策略，提升复杂交互中的灵活操作能力。
摘要：
在需要接触的任务中，生成能够产生全面触觉感知动作的策略能力至关重要。然而，现有的灵活操作数据收集和技能学习系统往往受到低维触觉信息的限制。为了解决这一限制，我们提出了DexTac，一种基于运动教学的视觉-触觉操作学习框架。DexTac直接从人类示范中捕捉多维触觉数据，包括接触力分布和空间接触区域。通过将这些丰富的触觉模态整合到策略网络中，所得到的接触感知代理使灵活的手能够在复杂的互动中自主选择和维持最优的接触区域。我们在具有挑战性的单手注射任务上评估了我们的框架。实验结果表明，DexTac实现了91.67%的成功率。值得注意的是，在涉及小型注射器的高精度场景中，我们的方法比仅使用力的基线方法高出31.67%。这些结果强调了从人类示范中学习多维触觉先验对于在接触丰富的环境中实现稳健、类人灵活操作的重要性。
英文摘要：
For contact-intensive tasks, the ability to generate policies that produce comprehensive tactile-aware motions is essential. However, existing data collection and skill learning systems for dexterous manipulation often suffer from low-dimensional tactile information. To address this limitation, we propose DexTac, a visuo-tactile manipulation learning framework based on kinesthetic teaching. DexTac captures multi-dimensional tactile data-including contact force distributions and spatial contact regions-directly from human demonstrations. By integrating these rich tactile modalities into a policy network, the resulting contact-aware agent enables a dexterous hand to autonomously select and maintain optimal contact regions during complex interactions. We evaluate our framework on a challenging unimanual injection task. Experimental results demonstrate that DexTac achieves a 91.67% success rate. Notably, in high-precision scenarios involving small-scale syringes, our approach outperforms force-only baselines by 31.67%. These results underscore that learning multi-dimensional tactile priors from human demonstrations is critical for achieving robust, human-like dexterous manipulation in contact-rich environments.
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。
