用自然语言描述损伤，让机器人零样本适应结构故障。
Zero-Shot Adaptation to Robot Structural Damage via Natural Language-Informed Kinodynamics Modeling
- 通过自监督学习将语言描述与运动行为对齐，构建动力学模型。
- 在模拟中实现81%的误差降低，跨仿真到现实和尺度泛化。
- 适合研究机器人自适应、故障容错或人机交互的开发者。
高性能自主移动机器人在野外作业中承受巨大机械应力，如高速行驶或穿越崎岖地形。尽管平台设计可耐受这些条件，但结构退化不可避免。结构损伤会导致运动动力学行为出现稳定且显著的变化。由于损伤形式多样，量化其对动力学的影响极具挑战。我们提出，自然语言可有效描述这类多样性损伤。因此，我们开发了零样本语言引导动力学建模（ZLIK），利用自监督学习将损伤描述的语义信息与动力学行为对齐，以数据驱动方式学习前向动力学模型。基于高保真软体物理引擎BeamNG.tech，我们采集了多种受损车辆的数据。所学模型实现对不同损伤的零样本适应，在动力学误差上最高降低81%，并成功跨越仿真到现实以及全尺寸到1/10尺寸的尺度差距。
原文摘要 · Abstract (English)
High-performance autonomous mobile robots endure significant mechanical stress during in-the-wild operations, e.g., driving at high speeds or over rugged terrain. Although these platforms are engineered to withstand such conditions, mechanical degradation is inevitable. Structural damage manifests as consistent and notable changes in kinodynamic behavior compared to a healthy vehicle. Given the heterogeneous nature of structural failures, quantifying various damages to inform kinodynamics is challenging. We posit that natural language can describe and thus capture this variety of damages. Therefore, we propose Zero-shot Language Informed Kinodynamics (ZLIK), which employs self-supervised learning to ground semantic information of damage descriptions in kinodynamic behaviors to learn a forward kinodynamics model in a data-driven manner. Using the high-fidelity soft-body physics simulator BeamNG.tech, we collect data from a variety of structurally compromised vehicles. Our learned model achieves zero-shot adaptation to different damages with up to 81% reduction in kinodynamics error and generalizes across the sim-to-real and full-to-1/10$^{\text{th}}$ scale gaps.
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