Solving partially observable Markov decision processes (POMDPs) with high dimensional
and continuous observations, such as camera images, is required for many real life robotics …

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) …

In centralized multi-agent systems, often modeled as multi-agent partially observable
Markov decision processes (MPOMDPs), the action and observation spaces grow …

In this paper, we present a controller framework that synthesizes control policies for Jump
Markov Linear Systems subject to stochastic mode switches and imperfect mode estimation …

Pragmatic or goal-oriented communication can optimize communication decisions beyond
the reliable transmission of data, instead aiming at directly affecting application performance …

Online planning under uncertainty remains a critical challenge in robotics and autonomous
systems. While tree search techniques are commonly employed to construct partial future …

Robots operating in complex and unknown environments frequently require geometric-
semantic representations of the environment to safely perform their tasks. While inferring the …

Risk averse decision making under uncertainty in partially observable domains is a
fundamental problem in AI and essential for reliable autonomous agents. In our case, the …

Planning in real-world settings often entails addressing partial observability while aligning
with users' preferences. We present a novel framework for expressing users' preferences …

Online planning under uncertainty in partially observable domains is an essential capability
in robotics and AI. The partially observable Markov decision process (POMDP) is a …