How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

Anirudha Majumdar, Marco Pavone

Research output: Chapter in Book/Report/Conference proceedingChapter

91 Scopus citations


Endowing robots with the capability of assessing risk and making risk-aware decisions is widely considered a key step toward ensuring safety for robots operating under uncertainty. But, how should a robot quantify risk? A natural and common approach is to consider the framework whereby costs are assigned to stochastic outcomes–an assignment captured by a cost random variable. Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping from the cost random variable to a real number. Yet, the question of what constitutes a “good” risk metric has received little attention within the robotics community. The goal of this paper is to explore and partially address this question by advocating axioms that risk metrics in robotics applications should satisfy in order to be employed as rational assessments of risk. We provide instantiations of the class of risk metrics that satisfy these axioms (referred to as distortion risk metrics) and also discuss pitfalls of commonly used risk metrics in robotics that do not satisfy these axioms. Our hope is that the ideas presented here will lead to a foundational framework for quantifying risk (and hence safety) in robotics applications.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Advanced Robotics
PublisherSpringer Science and Business Media B.V.
Number of pages10
StatePublished - 2020

Publication series

NameSpringer Proceedings in Advanced Robotics
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Artificial Intelligence
  • Engineering (miscellaneous)
  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


  • Planning and decision making under uncertainty
  • Risk metrics
  • Safety


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