Embodied Lifelong Learning for Task and Motion Planning

Conference on Robot Learning (CoRL), 2023
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Abstract: A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge to become a more proficient assistant. We formalize this setting with a novel lifelong learning problem formulation in the context of learning for task and motion planning (TAMP). Exploiting the modularity of TAMP systems, we develop a generative mixture model that produces candidate continuous parameters for a planner. Whereas most existing lifelong learning approaches determine a priori how data is shared across task models, our approach learns shared and non-shared models and determines which to use online during planning based on auxiliary tasks that serve as a proxy for each model’s understanding of a state. Our method exhibits substantial improvements in planning success on simulated 2D domains and on several problems from the BEHAVIOR benchmark.

Recommended citation:

@inproceedings{mendez2023embodied, 
 author = {Mendez-Mendez, Jorge and Kaelbling, Leslie Pack and Lozano-Perez, Tomas}, 
 booktitle = {Proceedings of the 7th Conference on Robot Learning (CoRL-23)}, 
 title = {Embodied Lifelong Learning for Task and Motion Planning}, 
 year = {2023} 
}