Risk-Averse Planning Under Uncertainty

Ahmadi, Mohamadreza; Ono, Masahiro; Ingham, Michel D.; Murray, Richard M.; Ames, Aaron D.

doi:10.23919/acc45564.2020.9147792

Cited by 22 publications

(24 citation statements)

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“…The method was applied to enforce risk-averse safety of a bipedal robot. Future work will extend the CVaR barrier functions to other coherent risk measures, continuoustime systems, and applications involving cooperative humanrobot teams and imperfect sensor measurements [34] and convex/polytopic approximations of barrier functions leading to convex programs.…”

Section: Discussionmentioning

confidence: 99%

Risk-Averse Control via CVaR Barrier Functions: Application to Bipedal Robot Locomotion

Ahmadi

Xiong

Ames

2022

IEEE Control Syst. Lett.

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Enforcing safety in the presence of stochastic uncertainty is a challenging problem. Traditionally, researchers have proposed safety in the statistical mean as a safety measure for systems subject to stochastic uncertainty. However, ensuring safety in the statistical mean is only reasonable if system's safe behavior in the large number of runs is of interest, which precludes the use of mean safety in practical scenarios. In this paper, we propose a risk sensitive notion of safety called conditional-value-atrisk (CVaR) safety. We introduce CVaR barrier functions as a tool to enforce CVaR-safety and propose conditions for their Boolean compositions. Given a legacy controller, we show that we can design a minimally interfering CVaR-safe controller via solving difference convex programs (DCPs). We elucidate the proposed method by applying it to a bipedal robot locomotion case study.

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Section: Discussionmentioning

confidence: 99%

Risk-Averse Control via CVaR Barrier Functions: Application to Bipedal Robot Locomotion

Ahmadi

Xiong

Ames

2022

IEEE Control Syst. Lett.

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“…Conditional value-at-risk (CVaR) is one such risk measure that has this desirable set of properties, and is a part of a class of risk metrics known as coherent risk measures [6] Coherent risk measures have been used in a variety of decision making problems, especially Markov decision processes (MDPs) [10]. In recent years, Ahmadi et al synthesized risk averse optimal policies for partially observable MDPs, constrained MDPs, and for shortest path problems in MDPs [4,3,5]. Coherent risk measures have been used in a MPC framework when the system model is uncertain [36] and when the uncertainty is a result of measurement noise or moving obstacles [12].…”

Section: Related Workmentioning

confidence: 99%

STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation

Fan¹,

Otsu²,

Kubo

et al. 2021

Robotics: Science and Systems XVII

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Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertaintyaware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC). We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an abandoned subway and an underground lava tube.

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“…Risk-averse dynamic programming methods that protect against large negative deviations from expected values have been developed for MDPs using average value-at-risk (VaR) [ 62 , 63 ]. Other coherent risk measures, including conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR), have recently been used in risk-averse planning and decision optimization algorithms for both MDPs and POMDPs [ 64 , 65 ], as well as in model-free RL algorthms [ 66 ].…”

Section: Structure Of Explanations For Decision Recommendations Based On Reinforcement Learning (Rl) With Initially Unknown or Uncertain mentioning

confidence: 99%

Information Structures for Causally Explainable Decisions

Cox

2021

Entropy

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For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales use causal models to link potential courses of action to resulting outcome probabilities. They reflect an understanding of possible actions, preferred outcomes, the effects of action on outcome probabilities, and acceptable risks and trade-offs—the standard ingredients of normative theories of decision-making under uncertainty, such as expected utility theory. Competent AI advisory systems should also notice changes that might affect a user’s plans and goals. In response, they should apply both learned patterns for quick response (analogous to fast, intuitive “System 1” decision-making in human psychology) and also slower causal inference and simulation, decision optimization, and planning algorithms (analogous to deliberative “System 2” decision-making in human psychology) to decide how best to respond to changing conditions. Concepts of conditional independence, conditional probability tables (CPTs) or models, causality, heuristic search for optimal plans, uncertainty reduction, and value of information (VoI) provide a rich, principled framework for recognizing and responding to relevant changes and features of decision problems via both learned and calculated responses. This paper reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to support responses on multiple time scales, and evaluate and update causal models and plans in light of new data. The resulting causally explainable decisions make efficient use of available information to achieve goals in uncertain environments.

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Risk-Averse Planning Under Uncertainty

Cited by 22 publications

References 50 publications

Risk-Averse Control via CVaR Barrier Functions: Application to Bipedal Robot Locomotion

Risk-Averse Control via CVaR Barrier Functions: Application to Bipedal Robot Locomotion

STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation

Information Structures for Causally Explainable Decisions

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