Tom Silver, Massachusetts Institute of Technology

Abstract

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.

[1] Learning symbolic operators for task and motion planning. Silver*, Chitnis*, Tenenbaum, Kaelbling, Lozano-Perez. IROS 2021.
[2] Learning neuro-symbolic relational transition models for bilevel planning. Chitnis*, Silver*, Tenenbaum, Lozano-Perez, Kaelbling. IROS 2022.
[3] Predicate invention for bilevel planning. Silver*, Chitnis*, Kumar, McClinton, Lozano-Perez, Kaelbling, Tenenbaum. AAAI 2023.
[4] Learning neuro-symbolic skills for bilevel planning. Silver, Athalye, Tenenbaum, Lozano-Perez, Kaelbling. CoRL 2022.

Bio: Tom Silver is a fifth year PhD student at MIT EECS advised by Leslie Kaelbling and Josh Tenenbaum. His research is at the intersection of machine learning and planning with applications to robotics, and often uses techniques from task and motion planning, program synthesis, and reinforcement learning. Before graduate school, he was a researcher at Vicarious AI and received his B.A. from Harvard in computer science and mathematics in 2016. His work is supported by an NSF fellowship and an MIT presidential fellowship.

Host: Kostas Bekris

This seminar is also on Zoom: https://rutgers.zoom.us/j/91941166267?pwd=NTNBdnc0V0QxYmg0VG8rSTZPVDBSZz09

Presented in association with the DATA-INSPIRE TRIPODS Institute.