We describe an integrated strategy for planning, perception, state estimation and action in
complex mobile manipulation domains based on planning in the belief space of probability …

In this paper, we describe an integrated strategy for planning, perception, state-estimation
and action in complex mobile manipulation domains. The strategy is based on planning in …

Spatial representations are fundamental to mobile robots operating in uncertain
environments. Two frequently-used representations are occupancy grid maps, which only …

This thesis is concerned with the task of autonomous selection of objects to remove (unload)
them from a pile in robotic manipulation systems. Applications such as the automation of …

Generative approaches to 3D scene perception and physical reasoning are increasingly
common [1, 2, 3, 4, 5, 6, 7]. There is a widespread need for scene models that can …

Robots performing service tasks such as cooking and cleaning in human-centric
environments require knowledge of certain environmental states in order to complete tasks …

The state-of-the-art object pose estimation approaches represent the set of detected poses
together with corresponding uncertainty. The inaccurate noisy poses may result in a …

In human-robot collaborative tasks, incorporating qualitative information provided by
humans can greatly enhance the robustness and efficacy of robot state estimation. We …

In this paper, we describe an integrated strategy for planning, perception, state-estimation
and action in complex mobile manipulation domains. The strategy is based on planning in …

Probabilistic circuits (PCs) are a family of tractable probabilistic models capable of
answering a wide range of queries exactly and in polytime. While inference is usually …