4 accepted papers
Robots often have to operate in discrete partially observable worlds, where the state of the world is only observable at runtime. To react to different world states, robots need contingencies. To find contingencies, prior work developed the path tree optimization (PTO) method, which computes motion…
Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Opt
This paper presents a new approach to Model Predictive Control for environments where essential, discrete variables are partially observed. Under this assumption, the belief state is a probability distribution over a finite number of states. We optimize a control-tree where each branch assumes a giv…
We propose a novel approach to Combined Task and Motion Planning (TAMP) under partial observability. Previous optimization-based TAMP methods [1][2] compute optimal plans and paths assuming full observability. However, partial observability requires the solution to be a policy that reacts to the obs…