Using tactile sensors for manipulation remains one of the most challenging problems in robotics. At the heart of these challenges is generalization: How can we train a tactile-based policy that can manipulate unseen and diverse objects? In this paper, we propose to perform Reinforcement Learning with only visual tactile sensing inputs on diverse objects in a physical simulator. By training with diverse objects in simulation, it enables the policy to generalize to unseen objects. However, leveraging simulation introduces the Sim2Real transfer problem. To mitigate this problem, we study different tactile representations and evaluate how each affects real-robot manipulation results after transfer. We conduct our experiments on diverse real-world objects and show significant improvements over baselines for the pivoting task. Our project page is available at https://tactilerl.github.io/.

翻译：使用触觉传感器进行操控仍然是机器人领域最具挑战性的问题之一。这些挑战的核心在于泛化能力：如何训练出能够操控未见过的多样化物体的触觉策略？本文提出在物理模拟器中仅使用视觉触觉传感输入，对多样化物体进行强化学习训练。通过在仿真中训练多样化物体，该策略能够泛化到未见物体。然而，利用仿真会引入仿真到现实迁移问题。为缓解此问题，我们研究了不同的触觉表征方式，并评估每种表征在迁移后对真实机器人操作结果的影响。我们在多种真实世界物体上进行实验，结果显示在旋转操作任务上相较于基线方法有显著提升。该项目页面见 https://tactilerl.github.io/。