The content provides an introduction to the use of tail risk measures in robotics, covering the following key points:
Limitations of the risk-neutral and worst-case paradigms in robotics: Risk-neutral approaches focus on average-case performance, while worst-case approaches can be overly conservative. Tail risk measures offer a middle ground, accounting for rare but high-impact events.
Definitions and properties of tail risk measures: The article defines key tail risk measures like Value-at-Risk, Conditional Value-at-Risk, and Entropic Value-at-Risk, highlighting their coherence properties and ability to capture extreme outcomes.
Risk-aware behavior planning: The content discusses how tail risk measures can be incorporated into Markov Decision Processes (MDPs) to find optimal risk-aware policies, considering both discounted and undiscounted (total cost) formulations.
Risk-aware motion planning and control: The article reviews how tail risk measures can be integrated into optimization-based motion planning and model predictive control (MPC) frameworks, enabling robots to navigate uncertain environments while accounting for safety and performance trade-offs.
Risk-aware verification: The importance of considering tail risk in the verification of robotic systems is highlighted, with an introduction to methods for safety-critical controller verification using tail risk measures.
The summary provides a comprehensive overview of the role of tail risk measures in enhancing the safety and reliability of autonomous robotic systems operating in complex, uncertain environments.
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