通过自然语言引导的运动动力学建模实现机器人结构损伤的零样本适应
Zero-Shot Adaptation to Robot Structural Damage via Natural Language-Informed Kinodynamics Modeling
AI总结：
本研究提出ZLIK方法，利用自然语言引导的自监督学习，通过数据驱动的方式学习运动动力学模型，实现对机器人结构损伤的零样本适应，显著降低运动动力学误差并提升跨尺度泛化能力。
摘要：
高性能的自主移动机器人在野外作业中会经历显著的机械应力，例如高速驾驶或在崎岖地形上行驶。尽管这些平台被设计成能够承受这样的条件，但机械退化是不可避免的。结构损伤表现为与健康车辆相比，运动动力学行为出现一致且显著的变化。鉴于结构故障的异质性，量化各种损伤以指导运动动力学具有挑战性。我们提出自然语言可以描述并因此捕捉这种各种损伤的多样性。因此，我们提出了零样本语言引导的运动动力学（ZLIK），该方法利用自监督学习将损伤描述的语义信息 grounding 在运动动力学行为中，以数据驱动的方式学习一个前向运动动力学模型。使用高保真的软体物理模拟器 BeamNG.tech，我们收集了各种结构受损车辆的数据。我们的学习模型在不同损伤上实现了零样本适应，运动动力学误差减少了高达81%，并且在仿真到现实以及全尺寸到1/10规模之间具有泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
