论文标题
可重新配置机器人操纵器的可延展代理
Malleable Agents for Re-Configurable Robotic Manipulators
论文作者
论文摘要
可重新配置的机器人对许多现实世界任务具有更多的实用性和灵活性。设计学习代理以操作此类机器人需要适应不同的配置。在这里，我们专注于具有通过关节连接的多个刚性链路的机器人臂。我们提出了一种深钢筋学习代理，其序列神经网络嵌入了代理中，以适应具有不同链接的机器人臂。此外，借助域随机化工具，该代理适应了不同的配置。我们在2D N-Link组上进行模拟，以显示网络有效传输和推广的能力。
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.
