Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft-actuated systems in under one minute. Architected materials are well equipped for adaptation: actuator failure occurs gradually rather than acutely, and damage can be described in a low-dimensional, discrete coordinate space. Surprisingly, latent damage representations plus a simple yet robust ensemble method is sufficient for adapting to unseen damage in real-time. Moreover, we identify conditions under which exponential sample complexity collapses to linear sample complexity for learned representations of architected materials, a concrete advantage over rigid components or continuum soft mechanisms. We demonstrate LEAP, our method for adaptive proprioception, via a tracing task for a 6DoF soft wrist based on Handed Shearing Auxetic (HSA) actuators. Our algorithm is able to adapt to cuts, burns, and actuator repairs, enabling simulation-free real-time adaptation that is critical for realizing the promise of soft robots outside the lab. Videos and more information are available at https://murpheylab.github.io/leap.

翻译：对损伤的自适应及原位物理修复对于长期机器人自主性至关重要，但在狭义定义和良好预期的范围之外却颇具挑战性。在这项工作中，我们在一分钟内通过本体感知自适应地处理软驱动系统中的灾难性损伤。架构材料具备良好的自适应能力：致动器故障是逐渐而非急剧发生的，且损伤可以在低维离散坐标空间中描述。令人惊讶的是，潜在损伤表征加上简单而稳健的集成方法足以实时适应未见过的损伤。此外，我们确定了架构材料学习表征中指数样本复杂度坍缩为线性样本复杂度的条件，这是相较于刚性部件或连续软机构的显著优势。我们通过基于手性剪切拉胀（HSA）致动器的六自由度软腕部追踪任务，展示了自适应本体感知方法LEAP。我们的算法能够适应切割、烧伤和致动器修复，实现了无需仿真的实时自适应，这对于在实验室外实现软机器人的应用前景至关重要。视频及更多信息请访问https://murpheylab.github.io/leap。