Task-Driven Estimation and Control via Information Bottlenecks

Vincent Pacelli, Anirudha Majumdar

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

Abstract

Our goal is to develop a principled and general algorithmic framework for task-driven estimation and control for robotic systems. State-of-the-art approaches for controlling robotic systems typically rely heavily on accurately estimating the full state of the robot (e.g., a running robot might estimate joint angles and velocities, torso state, and position relative to a goal). However, full state representations are often excessively rich for the specific task at hand and can lead to significant computational inefficiency and brittleness to errors in state estimation. In contrast, we present an approach that eschews such rich representations and seeks to create task-driven representations. The key technical insight is to leverage the theory of information bottlenecks to formalize the notion of a 'task-driven representation' in terms of information theoretic quantities that measure the minimality of a representation. We propose novel iterative algorithms for automatically synthesizing (offline) a task-driven representation (given in terms of a set of task-relevant variables (TRVs)) and a performant control policy that is a function of the TRVs. We presentonline algorithms for estimating the TRVs in order to apply the control policy. We demonstrate that our approach results in significant robustness to unmodeled measurement uncertainty both theoretically and via thorough simulation experiments including a spring-loaded inverted pendulum running to a goal location.

Original languageEnglish (US)
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2061-2067
Number of pages7
ISBN (Electronic)9781538660263
DOIs
StatePublished - May 2019
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: May 20 2019May 24 2019

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2019-May
ISSN (Print)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
CountryCanada
CityMontreal
Period5/20/195/24/19

All Science Journal Classification (ASJC) codes

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

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  • Cite this

    Pacelli, V., & Majumdar, A. (2019). Task-Driven Estimation and Control via Information Bottlenecks. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp. 2061-2067). [8794213] (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2019.8794213