1 citations · 2 across the 4 of their papers we have counts for
4 papers
LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
Aidan Curtis, Hao Tang, Thiago Veloso +4
Partially Observable Markov Decision Processes (POMDPs) model decision making under uncertainty. While there are many approaches to approximately solving POMDPs, we aim to address…
Seeing is Believing: Belief-Space Planning with Foundation Models as Uncertainty Estimators
Linfeng Zhao, Willie McClinton, Aidan Curtis +4
Generalizable robotic mobile manipulation in open-world environments poses significant challenges due to long horizons, complex goals, and partial observability. A promising approa…
Functional Risk Minimization
Ferran Alet, Clement Gehring, Tomás Lozano-Pérez +3
The field of Machine Learning has changed significantly since the 1970s. However, its most basic principle, Empirical Risk Minimization (ERM), remains unchanged. We propose Functio…
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning
Xiaolin Fang, Bo-Ruei Huang, Jiayuan Mao +4
Generalization to novel object configurations and instances across diverse tasks and environments is a critical challenge in robotics. Keypoint-based representations have been prov…