Tactile information is important for robust performance in robotic tasks that involve physical interaction, such as object manipulation. However, with more data included in the reasoning and control process, modeling behavior becomes increasingly difficult. Deep Reinforcement Learning (DRL) produced promising results for learning complex behavior in various domains, including tactile-based manipulation in robotics. In this work, we present our open-source reinforcement learning environments for the TIAGo service robot. They produce tactile sensor measurements that resemble those of a real sensorised gripper for TIAGo, encouraging research in transfer learning of DRL policies. Lastly, we show preliminary training results of a learned force control policy and compare it to a classical PI controller.

翻译：触觉信息对于涉及物理交互的机器人任务（如物体操作）实现稳健性能至关重要。然而，随着更多数据被纳入推理与控制过程，行为建模的难度逐渐增加。深度强化学习在包括机器人触觉操作在内的多个领域展现出学习复杂行为的良好前景。本文介绍了面向TIAGo服务机器人的开源强化学习环境。该环境可生成与真实TIAGo触觉夹爪传感器测量值相似的仿真数据，旨在促进深度强化学习策略迁移的研究。最后，我们展示了学习型力控策略的初步训练结果，并将其与经典PI控制器进行了对比。