Search Results for author:
Found 7 papers, 5 papers with code
Multi-Agent Reachability Calibration with Conformal Prediction
We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning.
Optimality Guarantees for Particle Belief Approximation of POMDPs
Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces.
Navigation between initial and desired community states using shortcuts
Using several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs; and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices.
Compositional Learning-based Planning for Vision POMDPs
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty.
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications.
Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs
This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs).
Sparse tree search optimality guarantees in POMDPs with continuous observation spaces
Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve.
