Optimizing Gait Libraries via a Coverage Metric
Optimizing Gait Libraries via a Coverage Metric
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发表时间：
2021-07
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通讯作者：
Brian Bittner;Shai Revzen
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作者：
Brian Bittner;Shai Revzen
许多机器人通过组成移动原语（如步进和转弯）在世界中移动。为了做到这一点，机器人不需要具有对人类具有直观意义的原语。当机器人损坏并且不再按设计移动时，这一点变得至关重要。在这里，我们提出了一个称为“覆盖”的目标函数，它以与基元本身的细节无关的方式表示运动基元库的有用性。我们展示了优化模拟和物理机器人覆盖范围的能力，并表明覆盖范围可以在受伤后快速恢复。这表明，通过优化覆盖范围，即使面临重大机械故障，机器人也可以维持其在世界中导航的能力。这种方法的好处通过样本高效、数据驱动的系统识别方法得到了增强，这些方法可以快速通知基元的优化。我们发现自由度的数量提高了模拟机器人的恢复率，这在步态优化和强化学习领域是罕见的成果。我们展示了四肢由树枝制成的机器人（没有可用的 CAD 模型或第一原理模型）能够快速找到有效的高覆盖率运动基元库。优化的基元对于人类观察者来说是完全不明显的，因此不太可能通过手动调整来实现。
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.