Re-configurable robots have more utility and flexibility for many real-world tasks. Designing a learning agent to operate such robots requires adapting to different configurations. Here, we focus on robotic arms with multiple rigid links connected by joints. We propose a deep reinforcement learning agent with sequence neural networks embedded in the agent 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. We perform simulations on a 2D N-link arm to show the ability of our network to transfer and generalize efficiently.
翻译:重新配置的机器人对于许多现实世界的任务具有更大的效用和灵活性。设计一个操作这种机器人的学习代理机构需要适应不同的配置。在这里,我们侧重于具有由连接连接的多重硬链接的机器人臂。我们提议一个深度强化学习代理机构,该代理机构内嵌有序列神经网络,以适应具有不同链接的机器人臂。此外,由于增加了域随机化工具,该代理机构适应了不同的配置。我们在2D N链接臂上进行模拟,以显示我们的网络有效传输和普及的能力。