Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conservative behavior, and tuning these controllers to find the ideal trade-off between performance and safety typically requires domain expertise or a carefully constructed reward function. This work presents a design paradigm for systematically achieving behaviors that balance performance and robust safety by integrating safety-aware Preference-Based Learning (PBL) with Control Barrier Functions (CBFs). Fusing these concepts -- safety-aware learning and safety-critical control -- gives a robust means to achieve safe behaviors on complex robotic systems in practice. We demonstrate the capability of this design paradigm to achieve safe and performant perception-based autonomous operation of a quadrupedal robot both in simulation and experimentally on hardware.
翻译:将动态机器人带入野外需要一种微弱的性能与安全平衡。然而,设计用于提供稳健安全保障的控制器往往导致保守行为,而调整这些控制器以寻找理想的性能与安全权衡,通常需要领域专门知识或精心构建的奖励功能。 这项工作为系统地实现平衡性能与安全的行为提供了一个设计模式,通过将安全意识优先学习(PBL)与控制屏障功能(CBFs)结合起来。 运用这些概念 -- -- 安全意识学习和安全关键控制 -- -- 提供了一种强有力的手段,在复杂的机器人系统中实现安全行为。 我们展示了这一设计模式在模拟和实验中实现四重机器人安全、以表现为主的自主操作的能力。