Both the design and control of a robot play equally important roles in its
task performance. However, while optimal control is well studied in the machine
learning and robotics community, less attention is placed on finding the
optimal robot design. This is mainly because co-optimizing design and control
in robotics is characterized as a challenging problem, and more importantly, a
comprehensive evaluation benchmark for co-optimization does not exist. In this
paper, we propose Evolution Gym, the first large-scale benchmark for
co-optimizing the design and control of soft robots. In our benchmark, each
robot is composed of different types of voxels (e.g., soft, rigid, actuators),
resulting in a modular and expressive robot design space. Our benchmark
environments span a wide range of tasks, including locomotion on various types
of terrains and manipulation. Furthermore, we develop several robot
co-evolution algorithms by combining state-of-the-art design optimization
methods and deep reinforcement learning techniques. Evaluating the algorithms
on our benchmark platform, we observe robots exhibiting increasingly complex
behaviors as evolution progresses, with the best evolved designs solving many
of our proposed tasks. Additionally, even though robot designs are evolved
autonomously from scratch without prior knowledge, they often grow to resemble
existing natural creatures while outperforming hand-designed robots.
Nevertheless, all tested algorithms fail to find robots that succeed in our
hardest environments. This suggests that more advanced algorithms are required
to explore the high-dimensional design space and evolve increasingly
intelligent robots -- an area of research in which we hope Evolution Gym will
accelerate progress. Our website with code, environments, documentation, and
tutorials is available at this http URL

本文提出第一个针对软性机器人设计和控制的大规模 benchmark Evolution Gym，提出了结合了最新设计优化方法和深度强化学习技术的机器人 co-evolution algorithms。在 benchmark 上测试算法，观察到随着进化的进行，机器人表现出越来越复杂的行为和设计，其中最佳设计成功解决了许多任务，但所有经过测试的算法均未能找到在最难的环境中成功的机器人，这表明需要更高级的算法来探索高维度设计空间和进化越来越智能的机器人，期望 Evolution Gym 能加速该领域的进展。