Online Replanning in Belief Space for Partially Observable Task and Motion Problems
Name
1911.04577.pdf
Description
Submitted version
Size
793.43 KB
Format
Adobe PDF
Checksum (MD5)
546c420c1d283c9285b185151fe4c74c
Author(s)
Garrett, Caelan Reed
Paxton, Chris
Lozano-Perez, Tomas
Kaelbling, Leslie Pack
Fox, Dieter
Date Issued
September 2020
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Garrett, Caelan Reed et al. "Online Replanning in Belief Space for Partially Observable Task and Motion Problems." IEEE International Conference on Robotics and Automation, May-August 2020, Paris, France, Institute of Electrical and Electronics Engineers, September 2020. © 2020 IEEE
Version
Original manuscript
Abstract
To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require opening a drawer to observe its contents or moving an object out of the way to examine the space behind it. Upon receiving a new observation, the robot must update its belief about the world and compute a new plan of action. In this work, we present an online planning and execution system for robots faced with these challenges. We perform deterministic cost-sensitive planning in the space of hybrid belief states to select likely-to-succeed observation actions and continuous control actions. After execution and observation, we replan using our new state estimate. We initially enforce that planner reuses the structure of the unexecuted tail of the last plan. This both improves planning efficiency and ensures that the overall policy does not undo its progress towards achieving the goal. Our approach is able to efficiently solve partially observable problems both in simulation and in a real-world kitchen.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra40945.2020.9196681
