Robot co-design, where the morphology of a robot is optimized jointly with a
learned policy to solve a specific task, is an emerging area of research. It
holds particular promise for soft robots, which are amenable to novel
manufacturing techniques that can realize learned morphologies and actuators.
Inspired by nature and recent novel robot designs, we propose to go a step
further and explore the novel reconfigurable robots, defined as robots that can
change their morphology within their lifetime. We formalize control of
reconfigurable soft robots as a high-dimensional reinforcement learning (RL)
problem. We unify morphology change, locomotion, and environment interaction in
the same action space, and introduce an appropriate, coarse-to-fine curriculum
that enables us to discover policies that accomplish fine-grained control of
the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark
for reconfigurable soft robots that require fine-grained morphology changes to
accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine
algorithm on DittoGym and demonstrate robots that learn to change their
morphology several times within a sequence, uniquely enabled by our RL
algorithm. More results are available at this https URL

机器人共同设计是一个新兴的研究领域，尤其在软体机器人方面有巨大应用潜力，本文提出了一种基于增强学习的高维度算法来控制软体机器人的形态变化，同时引入了一个 RL 基准，展示了通过该算法使机器人在序列中多次改变形态的方法。

DittoGym: 学习控制柔性变形机器人

DittoGym: Learning to Control Soft Shape-Shifting Robots

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.

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。