Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning

Anirudha Majumdar, Vincent Pacelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Our goal is to develop theory and algorithms for establishing fundamental limits on performance for a given task imposed by a robot’s sensors. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano inequality from information theory, we demonstrate that this quantity provides an upper bound on the highest achievable expected reward for one-step decision making tasks. We then extend this bound to multi-step problems via a dynamic programming approach. We present algorithms for numerically computing the resulting bounds, and demonstrate our approach on three examples: (i) the lava problem from the literature on partially observable Markov decision processes, (ii) an example with continuous state and observation spaces corresponding to a robot catching a freely-falling object, and (iii) obstacle avoidance using a depth sensor with non-Gaussian noise. We demonstrate the ability of our approach to establish strong limits on achievable performance for these problems by comparing our upper bounds with achievable lower bounds (computed by synthesizing or learning concrete control policies).

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems
EditorsKris Hauser, Dylan Shell, Shoudong Huang
PublisherMIT Press Journals
ISBN (Print)9780992374785
StatePublished - 2022
Externally publishedYes
Event18th Robotics: Science and Systems, RSS 2022 - New York City, United States
Duration: Jun 27 2022 → …

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Conference18th Robotics: Science and Systems, RSS 2022
Country/TerritoryUnited States
CityNew York City
Period6/27/22 → …

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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