从零进化灵巧机器人
Evolving Dexterous Robots from Scratch
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中文总结 AI 辅助
本研究从零进化自由形态机器人，通过对比学习、自回归模型、进化策略和强化学习，实现灵巧操作，并零样本转化为实体，达到进化机器人领域最先进水平。
中文摘要 AI 辅助
关于如何手动设计能够进行灵巧操作的智能体，我们知之甚少。一些设计原则已通过仔细研究动物如何操作物体而推断出来，但这些结构和行为迄今为止仍难以通过仿生学实现，且可能并非人工机器的最优选择。在此，我们进化出自由形态的机器人，用于拾取、握持、旋转和使用多种物体。与其他优化机器人手部的方法不同，我们不预设身体任何部分的存在、关节结构或几何形状。尽管诸如尾巴、喙、爪和钳子等熟悉的抓握形态可能在某些条件下自发涌现——尽管这些条件可能引起进化生物学家的兴趣——从头设计操作器也能揭示全新的解决方案，即那些可能更适合当前任务的被忽视或未知的结构。我们使用对比学习来创建设计空间的高度可搜索的基因嵌入，使用自回归发育模型来解码设计，使用进化策略来寻找优秀设计，并使用强化学习来训练每个进化出的设计。获胜设计被自动转换为可制造的蓝图，以零样本方式打印、组装并在现实世界中进行测试。结果代表了进化机器人在性能、多样性和复杂性方面的最先进水平。
英文摘要
Little is known about how to manually design agents capable of dexterous manipulation. Some design principles have been inferred from close examination of how animals manipulate objects, but these structures and behaviors have so far resisted biomimicry and may not be optimal for artificial machines. Here we evolve freeform robots to pick up, hold, rotate, and use diverse objects. Unlike other approaches to optimizing robot hands, we do not presuppose the presence, articulation, or geometry of any part of the body. Although familiar prehensile forms such as tails, beaks, paws and claws may emerge spontaneously under certain conditions--and while such conditions could be of interest to evolutionary biologists--de novo manipulator design can also reveal whole new solutions, overlooked or unknown structures which may be better suited for the task at hand. We use contrastive learning to create a highly searchable genetic embedding of design space, an autoregressive developmental model to decode designs, evolutionary strategies to find good designs, and reinforcement learning to train each evolved design. Winning designs were automatically converted into a manufacturable blueprint, printed, assembled and tested in the real world in a zero-shot manner. The results represent the state-of-the-art in evolutionary robotics in terms of performance, diversity and complexity.
