Peter Stone – Task-Motion Navigation Planning with Learning for Adaptable Mobile Service Robots
Channel: PRL Workshop – Planning and Reinforcement Learning
Date: 23/10/2020

Peter Stone, The University of Texas at Austin
Title: Task-Motion Navigation Planning with Learning for Adaptable Mobile Service Robots

Invited talk at the workshop "Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)" – ICAPS 2020
https://icaps20subpages.icaps-conference.org/workshops/prl/

Abstract:
Task-motion planning (TMP) addresses the problem of efficiently generating executable and low-cost task plans in a discrete space such that the (initially unknown) action costs are determined by motion plans in a corresponding continuous space. A task-motion plan for a mobile service robot that behaves in a highly dynamic domain can be sensitive to domain uncertainty and changes, leading to suboptimal behaviors or execution failures. This talk examines the ways in which machine learning can be integrated into a TMP system to increase generality and robustsness, with particular focus on navigation planning problems.