Bimanual manipulation with tactile feedback will be key to human-level robot dexterity. However, this topic is less explored than single-arm settings, partly due to the availability of suitable hardware along with the complexity of designing effective controllers for tasks with relatively large state-action spaces. Here we introduce a dual-arm tactile robotic system (Bi-Touch) based on the Tactile Gym 2.0 setup that integrates two affordable industrial-level robot arms with low-cost high-resolution tactile sensors (TacTips). We present a suite of bimanual manipulation tasks tailored towards tactile feedback: bi-pushing, bi-reorienting and bi-gathering. To learn effective policies, we introduce appropriate reward functions for these tasks and propose a novel goal-update mechanism with deep reinforcement learning. We also apply these policies to real-world settings with a tactile sim-to-real approach. Our analysis highlights and addresses some challenges met during the sim-to-real application, e.g. the learned policy tended to squeeze an object in the bi-reorienting task due to the sim-to-real gap. Finally, we demonstrate the generalizability and robustness of this system by experimenting with different unseen objects with applied perturbations in the real world. Code and videos are available at https://sites.google.com/view/bi-touch/.

翻译：具有触觉反馈的双臂操作将是实现人类级机器人灵巧性的关键。然而，相较于单臂场景，该主题的研究较少，部分原因在于合适硬件的可用性以及为具有较大状态-动作空间的任务设计有效控制器的复杂性。本文基于Tactile Gym 2.0平台，介绍了一种双臂触觉机器人系统（Bi-Touch），该系统集成了两个经济型工业级机器人手臂与低成本高分辨率触觉传感器（TacTips）。我们提出了一套针对触觉反馈定制的双臂操作任务：双推、双重新定向和双收集。为学习有效策略，我们为这些任务设计了恰当的奖励函数，并提出了一种基于深度强化学习的新型目标更新机制。同时，我们通过触觉仿真到现实方法将这些策略应用于真实场景。我们的分析重点指出了仿真到现实迁移过程中遇到的若干挑战（例如在双重新定向任务中，由于仿真与现实差异，学习到的策略倾向于挤压物体）并进行了探讨。最后，通过在真实世界中操作不同未见过物体并施加扰动，我们验证了本系统的泛化能力和鲁棒性。代码和视频见 https://sites.google.com/view/bi-touch/。