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,能够利用这一能力解决仅凭视觉观察无法完成的挑战性、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型输入模态。近年来,触觉传感器成本降低并得到广泛普及,但如何将其集成至控制回路仍是研究热点,核心挑战在于任务的部分可观测性及其接触密集特性。本研究提出使用强化学习方法学习端到端策略,将触觉传感器读数直接映射为动作指令。具体而言,我们在Franka Research 3机器人上针对具有挑战性的部分可观测插入任务,分别在仿真和真实系统中应用Dreamer-v3算法。为支撑真实实验,我们搭建了具备完全自主复位能力的机器人平台,可进行无需人工监督的大规模训练。初步结果表明,Dreamer算法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,我们发现提供触觉反馈通常能提升任务表现,尽管本实验中尚未融合其他感知模态。未来计划利用该平台评估更多强化学习算法在触觉任务中的表现。