The sense of touch is an essential ability for skillfully performing a variety of tasks, providing the capacity to search and manipulate objects without relying on visual information. Extensive research has been conducted over time to apply these human tactile abilities to robots. In this paper, we introduce a multi-finger robot system designed to search for and manipulate objects using the sense of touch without relying on visual information. Randomly located target objects are searched using tactile sensors, and the objects are manipulated for tasks that mimic daily-life. The objective of the study is to endow robots with human-like tactile capabilities. To achieve this, binary tactile sensors are implemented on one side of the robot hand to minimize the Sim2Real gap. Training the policy through reinforcement learning in simulation and transferring the trained policy to the real environment, we demonstrate that object search and manipulation using tactile sensors is possible even in an environment without vision information. In addition, an ablation study was conducted to analyze the effect of tactile information on manipulative tasks. Our project page is available at https://lee-kangwon.github.io/dextouch/

翻译：触觉是灵巧完成多种任务的关键能力，它使人类无需依赖视觉信息即可搜索和操作物体。长期以来，大量研究致力于将这种人类触觉能力应用于机器人。本文提出了一种多指机器人系统，该系统仅依靠触觉（不依赖视觉信息）完成物体的搜索与操作。系统通过触觉传感器搜索随机放置的目标物体，并模拟日常生活中的任务对其进行操作。本研究旨在赋予机器人类人触觉能力。为降低Sim2Real差距，我们在机器人手部一侧部署了二进制触觉传感器。通过在仿真环境中采用强化学习训练策略，并将训练后的策略迁移至真实环境，我们证明了即使在无视觉信息的环境中，利用触觉传感器进行物体搜索与操作是可行的。此外，我们还进行了消融实验以分析触觉信息对操作任务的影响。项目页面详见 https://lee-kangwon.github.io/dextouch/