Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

Haimin Hu, Jaime F. Fisac

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

5 Scopus citations

Abstract

The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people’s goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between robot trajectory optimization and human intent inference. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive human models with both continuous and categorical uncertainty. The efficacy of our approach is demonstrated with simulated driving examples.

Original languageEnglish (US)
Title of host publicationAlgorithmic Foundations of Robotics XV - Proceedings of the Fifteenth Workshop on the Algorithmic Foundations of Robotics
EditorsSteven M. LaValle, Jason M. O’Kane, Michael Otte, Dorsa Sadigh, Pratap Tokekar
PublisherSpringer Nature
Pages385-401
Number of pages17
ISBN (Print)9783031210891
DOIs
StatePublished - 2023
Externally publishedYes
Event15th Workshop on the Algorithmic Foundations of Robotics, WAFR 2022 - College Park, United States
Duration: Jun 22 2022Jun 24 2022

Publication series

NameSpringer Proceedings in Advanced Robotics
Volume25 SPAR
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Conference

Conference15th Workshop on the Algorithmic Foundations of Robotics, WAFR 2022
Country/TerritoryUnited States
CityCollege Park
Period6/22/226/24/22

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

Keywords

  • Dual control theory
  • Human-robot interaction
  • Stochastic MPC

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