Probabilistically Safe Robot Planning with Confidence-Based Human Predictions

Jaime F. Fisac, Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil, Steven Wang, Claire J. Tomlin, Anca D. Dragan

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

49 Scopus citations

Abstract

In order to safely operate around humans, robots can employ predictive models of human motion. Unfortunately, these models cannot capture the full complexity of human behavior and necessarily introduce simplifying assumptions. As a result, predictions may degrade whenever the observed human behavior departs from the assumed structure, which can have negative implications for safety. In this paper, we observe that how “rational” human actions appear under a particular model can be viewed as an indicator of that model’s ability to describe the human’s current motion. By reasoning about this model confidence in a real-time Bayesian framework, we show that the robot can very quickly modulate its predictions to become more uncertain when the model performs poorly. Building on recent work in provably-safe trajectory planning, we leverage these confidence-aware human motion predictions to generate assured autonomous robot motion. Our new analysis combines worst-case tracking error guarantees for the physical robot with probabilistic time-varying human predictions, yielding a quantitative, probabilistic safety certificate. We demonstrate our approach with a quadcopter navigating around a human.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XIV
EditorsHadas Kress-Gazit, Siddhartha S. Srinivasa, Tom Howard, Nikolay Atanasov
PublisherMIT Press Journals
ISBN (Print)9780992374747
DOIs
StatePublished - 2018
Externally publishedYes
Event14th Robotics: Science and Systems, RSS 2018 - Pittsburgh, United States
Duration: Jun 26 2018Jun 30 2018

Publication series

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

Conference

Conference14th Robotics: Science and Systems, RSS 2018
Country/TerritoryUnited States
CityPittsburgh
Period6/26/186/30/18

All Science Journal Classification (ASJC) codes

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

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