3 papers
cs.RO2024
SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine
Sadanand Modak, Noah Patton, Isil Dillig +1
This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user specific preferences (e.g. "good pull-over location") from visual…
cs.PL2023
Programming-by-Demonstration for Long-Horizon Robot Tasks
Noah Patton, Kia Rahmani, Meghana Missula +2
The goal of programmatic Learning from Demonstration (LfD) is to learn a policy in a programming language that can be used to control a robot's behavior from a set of user demonstr…
cs.LG2021
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
Noah Patton, Jihwan Jeong, Michael Gimelfarb +1
Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional dom…