Data-Driven Safety Verification and Explainability for Whole-Body Manipulation and Locomotion

Junhyeok Ahn, S. Bang, Carlos Gonzalez, Yuanchen Yuan, L. Sentis
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Abstract

Planning safe motions for legged robots requires sophisticated safety verification tools. However, designing such tools for such complex systems is challenging due to the nonlinear and high-dimensional nature of these systems' dynamics. In this paper, we present a probabilistic verification framework for legged systems, which evaluates the safety of planned trajectories by learning an assessment function from trajectories collected from a closed-loop system. Our approach does not require an analytic expression of the closed-loop dynamics, thus enabling safety verification of systems with complex models and controllers. Our framework consists of an offline stage that initializes a safety assessment function by simulating a nominal model and an online stage that adapts the function to address the sim-to-real gap. The performance of the proposed approach for safety verification is demonstrated using a quadruped balancing task and a humanoid reaching task. The results demonstrate that our framework accurately predicts the systems' safety both at the planning phase to generate robust trajectories and at execution phase to detect unexpected external disturbances.
数据驱动的全身操作和运动的安全性验证和可解释性
规划有腿机器人的安全运动需要复杂的安全验证工具。然而,由于这些系统动力学的非线性和高维性质,为这些复杂系统设计这样的工具是具有挑战性的。在本文中,我们提出了一个概率验证框架,该框架通过学习从闭环系统中收集的轨迹的评估函数来评估计划轨迹的安全性。我们的方法不需要闭环动力学的解析表达式,因此能够对具有复杂模型和控制器的系统进行安全验证。我们的框架包括一个离线阶段,该阶段通过模拟标称模型来初始化安全评估函数,以及一个在线阶段,该阶段调整该函数以解决模拟与真实的差距。采用四足平衡任务和人形到达任务演示了所提出的安全验证方法的性能。结果表明,我们的框架准确地预测了系统在规划阶段的安全性,以生成鲁棒轨迹,并在执行阶段检测意外的外部干扰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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