Neuro-symbolic learning for bilevel planning
Tom Silver (MIT)
- 14:00 25th January 2023 ( week 1, Hilary Term 2023 )Room 051
Decision-making in robotics domains is complicated by continuous state and action spaces, long horizons, and sparse feedback. One way to address these challenges is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. In this talk, I will give an overview of our recent efforts [1, 2, 3, 4] to design a bilevel planning system with state and action abstractions that are learned from data. I will also make the case for learning abstractions that are compatible with highly optimized PDDL planners, while arguing that PDDL planning should be only one component of a larger integrated planning system.