Humans have exceptional tactile sensing capabilities, which they can leverage to solve challenging, partially observable tasks that cannot be solved from visual observation alone. Research in tactile sensing attempts to unlock this new input modality for robots. Lately, these sensors have become cheaper and, thus, widely available. At the same time, the question of how to integrate them into control loops is still an active area of research, with central challenges being partial observability and the contact-rich nature of manipulation tasks. In this study, we propose to use Reinforcement Learning to learn an end-to-end policy, mapping directly from tactile sensor readings to actions. Specifically, we use Dreamer-v3 on a challenging, partially observable robotic insertion task with a Franka Research 3, both in simulation and on a real system. For the real setup, we built a robotic platform capable of resetting itself fully autonomously, allowing for extensive training runs without human supervision. Our preliminary results indicate that Dreamer is capable of utilizing tactile inputs to solve robotic manipulation tasks in simulation and reality. Furthermore, we find that providing the robot with tactile feedback generally improves task performance, though, in our setup, we do not yet include other sensing modalities. In the future, we plan to utilize our platform to evaluate a wide range of other Reinforcement Learning algorithms on tactile tasks.

翻译：人类拥有卓越的触觉感知能力，能够利用这种能力解决仅凭视觉观察无法完成的、具有挑战性的部分可观测任务。触觉传感研究致力于为机器人解锁这一新的输入模态。近年来，这类传感器成本逐渐降低，因而得到广泛应用。然而，如何将其整合到控制回路中仍是当前研究的活跃领域，其核心挑战在于操作任务的部分可观测性及密集接触特性。本研究提出使用强化学习来训练端到端策略，直接将触觉传感器读数映射为动作指令。具体而言，我们在仿真环境与真实系统中，使用Dreamer-v3算法在Franka Research 3机器人上执行具有挑战性的部分可观测插入任务。针对真实实验平台，我们构建了能够完全自主重置的机器人系统，从而实现无需人工监督的大规模训练。初步结果表明，Dreamer能够利用触觉输入解决仿真与真实环境中的机器人操作任务。此外，我们发现为机器人提供触觉反馈通常能提升任务性能，尽管在当前实验设置中尚未整合其他传感模态。未来，我们计划利用该平台在触觉任务上评估更广泛的强化学习算法。