Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the performance depends on a sample-inefficient procedure or human demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the agent to use information from the robot's forward kinematics and an uncalibrated camera. Furthermore, we improve the performance by efficiently adapting the meta-trained agent to use data from force/torque sensor. Finally, we propose an adaptation procedure for out-of-distribution tasks whose parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-based meta RL agent in PiH assembly tasks compared to previous approaches.

翻译：自主装配是工业机器人与服务机器人的核心能力之一，其中轴孔插入任务属于基础性装配操作。然而，在未知环境中执行轴孔装配仍面临挑战，这主要源于传感器噪声导致的任务参数（如孔位位置与姿态）不确定性。尽管已有研究提出基于情境的元强化学习方法以适应轴孔装配任务中的未知参数，但其性能依赖于样本效率低下的训练流程或人类示范数据。为提升元强化学习在真实轴孔装配任务中的适用性，本文提出通过机器人正向运动学信息与未标定相机数据训练智能体。此外，我们通过高效适配元训练智能体利用力/力矩传感器数据来提升性能。最后，针对参数分布偏离训练任务范围的异常任务，我们提出了专门的适配流程。仿真与实体机器人实验表明：相较于现有方法，本文改进方案在元训练样本效率、实际环境适配性能以及基于情境的元强化学习智能体在轴孔装配任务中的泛化能力方面均获得显著提升。