ViTaMIn：通过无机器人视觉-触觉操作界面学习富接触任务
ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface
AI总结：
针对富接触灵巧操作任务的演示数据收集成本高的问题，研究提出集成视觉触觉的手持无机器人界面ViTaMIn及多模态预训练策略，提升数据效率与任务表现。
摘要：
触觉信息对人类和机器人有效与环境交互至关重要，尤其针对需要理解接触属性的任务。解决这类灵巧操作任务通常依赖从演示数据集进行模仿学习，而这类数据集一般通过遥操作系统收集，往往需要大量时间与精力。为应对这些挑战，我们提出ViTaMIn——一种无本体的操作界面，它将视觉与触觉传感无缝集成到手持夹爪中，无需遥操作即可完成数据收集。我们的设计采用带触觉传感的柔顺Fin Ray夹爪，让操作者在操作过程中能感知力反馈，实现更直观的操作。此外，我们提出一种多模态表示学习策略以获取预训练触觉表示，提升了数据效率与策略鲁棒性。在7项富接触操作任务上的实验表明，ViTaMIn显著优于基线方法，证明了其在复杂操作任务上的有效性。
英文摘要：
Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact properties. Solving such dexterous manipulation tasks often relies on imitation learning from demonstration datasets, which are typically collected via teleoperation systems and often demand substantial time and effort. To address these challenges, we present ViTaMIn, an embodiment-free manipulation interface that seamlessly integrates visual and tactile sensing into a hand-held gripper, enabling data collection without the need for teleoperation. Our design employs a compliant Fin Ray gripper with tactile sensing, allowing operators to perceive force feedback during manipulation for more intuitive operation. Additionally, we propose a multimodal representation learning strategy to obtain pre-trained tactile representations, improving data efficiency and policy robustness. Experiments on seven contact-rich manipulation tasks demonstrate that ViTaMIn significantly outperforms baseline methods, demonstrating its effectiveness for complex manipulation tasks.
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。
