Many robots move through the world by composing locomotion primitives like steps and turns. To do so well, robots need not have primitives that make intuitive sense to humans. This becomes of paramount importance when robots are damaged and no longer move as designed. Here we propose a goal function we call "coverage", that represents the usefulness of a library of locomotion primitives in a manner agnostic to the particulars of the primitives themselves. We demonstrate the ability to optimize coverage on both simulated and physical robots, and show that coverage can be rapidly recovered after injury. This suggests that by optimizing for coverage, robots can sustain their ability to navigate through the world even in the face of significant mechanical failures. The benefits of this approach are enhanced by sample-efficient, data-driven approaches to system identification that can rapidly inform the optimization of primitives. We found that the number of degrees of freedom improved the rate of recovery of our simulated robots, a rare result in the fields of gait optimization and reinforcement learning. We showed that a robot with limbs made of tree branches (for which no CAD model or first principles model was available) is able to quickly find an effective high-coverage library of motion primitives. The optimized primitives are entirely non-obvious to a human observer, and thus are unlikely to be attainable through manual tuning.

翻译：许多机器人通过制造像步和转动这样的摇动原始材料在世界上移动。 要做到这一点, 机器人不需要拥有对人类具有直觉感知力的原始材料。 当机器人被损坏, 并且不再像设计的那样移动时, 这变得至关重要。 我们在这里提议了一个目标功能, 我们称之为“ 覆盖 ”, 代表着一个移动原始材料图书馆的有用性, 以与原始人本身的具体情况相适应的方式, 代表着移动原始材料图书馆的实用性。 我们展示了优化模拟机器人和物理机器人的覆盖范围的能力, 并显示在受伤后可以迅速恢复覆盖。 这意味着, 优化覆盖, 机器人就可以保持自己在世界上航行的能力, 即使在面临重大机械故障的情况下也是如此。 这种方法的优点是, 通过抽样高效、 数据驱动的系统识别方法, 能够迅速为原始材料提供优化信息。 我们发现, 自由度的数量提高了我们模拟机器人的恢复率,这是在游戏优化和强化学习领域的一个罕见的结果。 我们显示, 一个拥有树枝的机器人能够找到一个树枝部的机器人( 因为没有 CAD模型, 最原始的模型是无法实现最优化的模型。