Abstract— Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, they make restrictive assumptions that limit their applic...
Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...
— This paper presents the ResolveSpatialConstraints (RSC) algorithm for manipulation planning in a domain with movable obstacles. Empirically we show that our algorithm quickly g...
Mike Stilman, Jan-Ullrich Schamburek, James Kuffne...
This paper explores a topological perspective of planning in the presence of uncertainty, focusing on tasks specified by goal states in discrete spaces. The paper introduces stra...
We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred mot...