Re-configurable robots potentially have more utility and flexibility for many real-world tasks. Designing a learning agent to operate such robots requires adapting to different configurations. While deep reinforcement learning has had immense success in robotic manipulation, domain adaptation is a significant problem that limits its applicability to real-world robotics. We focus on robotic arms with multiple rigid links connected by joints. Recent attempts have performed domain adaptation and Sim2Real transfer to provide robustness to robotic arm dynamics and sensor/camera variations. However, there have been no previous attempts to adapt to robotic arms with a varying number of links. We propose an RL agent with sequence neural networks embedded in the deep neural network to adapt to robotic arms that have a varying number of links. Further, with the additional tool of domain randomization, this agent adapts to different configurations with varying number/length of links and dynamics noise. We perform simulations on a 2D N-link arm to show the ability of our network to transfer and generalize efficiently.

本文提出一种基于深度强化学习和序列神经网络的学习代理，应用于多连杆机械臂的不同配置。通过领域随机化方法进行训练，演示了该网络在2D N-Link机械臂上的泛化能力。

可塑性代理程序用于可重构机械机械臂