24 citations · 88 across the 31 of their papers we have counts for
32 papers
POMDPPlanners: Open-Source Package for POMDP Planning
Yaacov Pariente, Vadim Indelman
We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates…
A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces
Moshe Shienman, Ohad Levy-Or, Michael Kaess +1
We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages \slices from high-dimensional surfa…
Action-Consistent Decentralized Belief Space Planning with Inconsistent Beliefs and Limited Data Sharing: Framework and Simplification Algorithms with Formal Guarantees
Tanmoy Kundu, Moshe Rafaeli, Anton Gulyaev +1
In multi-robot systems, ensuring safe and reliable decision making under uncertain conditions demands robust multi-robot belief space planning (MR-BSP) algorithms. While planning w…
Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice
Idan Lev-Yehudi, Moran Barenboim, Vadim Indelman
Solving partially observable Markov decision processes (POMDPs) with high dimensional and continuous observations, such as camera images, is required for many real life robotics an…
Measurement Simplification in ρ-POMDP with Performance Guarantees
Tom Yotam, Vadim Indelman
Decision making under uncertainty is at the heart of any autonomous system acting with imperfect information. The cost of solving the decision making problem is exponential in the…
Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees
Moran Barenboim, Idan Lev-Yehudi, Vadim Indelman
Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). H…