Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

Robot planning in partially observable domains is difficult, because a robot needs to
estimate the current state and plan actions at the same time. When the domain includes …

Searching for objects is a fundamental skill for robots. As such, we expect object search to
eventually become an off-the-shelf capability for robots, similar to eg, object detection and …

This paper proposes an informative trajectory planning approach, namely, adaptive particle
filter tree with sigma point-based mutual information reward approximation (ASPIRe), for …

Autonomous mobile robots employed in industrial applications often operate in complex and
uncertain environments. In this paper we propose an approach based on an extension of …

Efficiently searching for target objects in complex environments that contain various types of
furniture, such as shelves, tables, and beds, is crucial for mobile robots, but it poses …

In this paper, we present pomdp_py, a general purpose Partially Observable Markov
Decision Process (POMDP) library written in Python and Cython. Existing POMDP libraries …