This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. The compliance of soft robots provides a form of "mechanical intelligence" -- the ability to passively exhibit behaviors that would otherwise be difficult to program. Exploiting this capacity requires careful consideration of the coupling between mechanical design and control. Co-optimization provides a promising means to generate sophisticated soft robots by reasoning over this coupling. However, the complex nature of soft robot dynamics makes it difficult to provide a simulation environment that is both sufficiently accurate to allow for sim-to-real transfer, while also being fast enough for contemporary co-optimization algorithms. In this work, we show that finite element simulation combined with recent model order reduction techniques provide both the efficiency and the accuracy required to successfully learn effective soft robot design-control pairs that transfer to reality. We propose a reinforcement learning-based framework for co-optimization and demonstrate successful optimization, construction, and zero-shot sim-to-real transfer of several soft crawling robots. Our learned robot outperforms an expert-designed crawling robot, showing that our approach can generate novel, high-performing designs even in well-understood domains.
翻译:这项工作为模拟、 共同优化和控制软腿机器人的设计和控制提供了一个完整的框架。 软机器人的合规性提供了一种“ 机械智能” 的“ 机械智能” 形式, 即能够被动地展示否则难以编程的行为。 利用这一能力需要仔细考虑机械设计和控制之间的结合。 共同优化性为通过推理这种结合产生先进的软机器人提供了一种有希望的手段。 然而,软机器人动态的复杂性使得难以提供既能足够精确地进行模拟环境,既能模拟到真实的转让,又能足够快地进行当代联合优化算法。 在这项工作中,我们表明,将有限要素模拟与最近的减少订单技术相结合,既能提供成功学习有效的软机器人设计控制配对所需的效率和准确性,又能向现实转变。 我们提议了一个强化学习基础框架,用于共同优化,并展示成功的优化、构建和零瞄准的模拟环境环境环境,既能让一些软爬行的机器人能够快速地转换,又能展示我们所学过的机器人的机器人在新的领域里造型。