通过人体教学捕捉多维触觉信息，提升机械手在接触任务中的精准操作能力。
DexTac: Learning Contact-aware Visuotactile Policies via Hand-by-hand Teaching
- 基于肢体教学采集人类演示中的多维触觉数据
- 在单手注射任务中达成91.67%成功率，小针头场景领先31.67%
- 适合需要高精度触觉反馈的灵巧操作研究者
针对接触密集型任务，生成具备全面触觉感知的运动策略至关重要。然而，现有灵巧操作的数据采集与技能学习系统常受限于低维触觉信息。为此，我们提出DexTac，一种基于肢体教学的视觉-触觉操作学习框架。DexTac直接从人类示范中捕获多维触觉数据，包括接触力分布与空间接触区域。通过将这些丰富的触觉模态整合进策略网络，所得到的接触感知智能体能使灵巧手在复杂交互过程中自主选择并维持最优接触区域。我们在一项具有挑战性的单手注射任务上评估该框架。实验结果表明，DexTac在该任务上实现了91.67%的成功率。值得注意的是，在涉及小尺度注射器的高精度场景中，本方法相较仅使用力反馈的基线模型性能提升31.67%。这些结果表明，从人类示范中学习多维触觉先验对于实现接触密集环境中鲁棒且类人化的灵巧操作至关重要。
原文摘要 · Abstract (English)
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.
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