We present a new approach for finding generalized contingent plans with loops and branches in situations where there is uncertainty in state properties and object quantities, but ...
Siddharth Srivastava, Neil Immerman, Shlomo Zilber...
Abstract— The Rapidly-exploring Random Tree (RRT) algorithm has found widespread use in the field of robot motion planning because it provides a single-shot, probabilistically c...
Matthew Zucker, James J. Kuffner, Michael S. Brani...
Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabi...
During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), have been shown to work well in practice and to po...