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Learning to accelerate planning
Even with good abstractions, online planning can be slow, especially in high-dimensional environments with many objects. Robots should learn to plan better and faster over time. We can automatically accelerate planning by learning object-centric task abstractions, learning to self-impose constraints, or learning heuristics.
Planning to learn
Robots should plan to practice to get better at planning. They should rapidly learn to specialize to the objects, goals, preferences, and constraints that are unique to their deployment. We can plan to learn samplers, predicates, and operators for bilevel planning. Our ultimate goal is to create a virtuous cycle of learning and planning.
code
I am a big fan of open-source code and open science. I typically develop research projects out in the open, not in private repos. You can find the code for all past research projects led by me on my GitHub or linked from the respective papers. Also check out my lab's mono-repo, which has a number of shared utilities for planning, learning, and simulation.