Guided search for task and motion plans using learned heuristics

Rohan Chitnis, Dylan Hadfield-Menell, Abhishek Gupta, Siddharth Srivastava, Edward Groshev, Christopher Lin, Pieter Abbeel

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

11 Scopus citations

Abstract

Tasks in mobile manipulation planning often require thousands of individual motions to complete. Such tasks require reasoning about complex goals as well as the feasibility of movements in configuration space. In discrete representations, planning complexity is exponential in the length of the plan. In mobile manipulation, parameters for an action often draw from a continuous space, so we must also cope with an infinite branching factor. Task and motion planning (TAMP) methods integrate logical search over high-level actions with geometric reasoning to address this challenge. We present an algorithm that searches the space of possible task and motion plans and uses statistical machine learning to guide the search process. Our contributions are as follows: 1) we present a complete algorithm for TAMP; 2) we present a randomized local search algorithm for plan refinement that is easily formulated as a Markov decision process (MDP); 3) we apply reinforcement learning (RL) to learn a policy for this MDP; 4) we learn from expert demonstrations to efficiently search the space of high-level task plans, given options that address different (potential) infeasibilities; and 5) we run experiments to evaluate our system in a variety of simulated domains. We show significant improvements in performance over prior work.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages447-454
Number of pages8
Volume2016-June
ISBN (Electronic)9781467380263
DOIs
StatePublished - Jun 8 2016
Externally publishedYes
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: May 16 2016May 21 2016

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
CountrySweden
CityStockholm
Period5/16/165/21/16

ASJC Scopus subject areas

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

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    Chitnis, R., Hadfield-Menell, D., Gupta, A., Srivastava, S., Groshev, E., Lin, C., & Abbeel, P. (2016). Guided search for task and motion plans using learned heuristics. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 (Vol. 2016-June, pp. 447-454). [7487165] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2016.7487165