8 papers
Online Risk-Averse Planning in POMDPs Using Iterated CVaR Value Function
Yaacov Pariente, Vadim Indelman
We study risk-sensitive planning under partial observability using the dynamic risk measure Iterated Conditional Value-at-Risk (ICVaR). A policy evaluation algorithm for ICVaR is d…
Towards Optimal Performance and Action Consistency Guarantees in Dec-POMDPs with Inconsistent Beliefs and Limited Communication
Moshe Rafaeli Shimron, Vadim Indelman
Multi-agent decision-making under uncertainty is fundamental for effective and safe autonomous operation. In many real-world scenarios, each agent maintains its own belief over the…
Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit +1
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for…
Bounding Conditional Value-at-Risk via Auxiliary Distributions with Bounded Discrepancies
Yaacov Pariente, Vadim Indelman
In this paper, we develop a theoretical framework for bounding the CVaR of a random variable using another related random variable , under assumptions on their cumulative an…
Anytime Incremental POMDP Planning in Continuous Spaces
Ron Benchetrit, Idan Lev-Yehudi, Andrey Zhitnikov +1
Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic expl…
Online Hybrid-Belief POMDP with Coupled Semantic-Geometric Models
Tuvy Lemberg, Vadim Indelman
Robots operating in complex and unknown environments frequently require geometric-semantic representations of the environment to safely perform their tasks. While inferring the env…