We present a new planning algorithm that formulates the planning problem as a counting satisfiability problem in which the number of available solutions guides the planner determ...
In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
We present deterministic sequences for use in sampling-based approaches to motion planning. They simultaneously combine the qualities found in many other sequences: i) the increme...
Abstract The problem of generating uniform deterministic samples over the rotation group, SO(3), is fundamental to many fields, such as computational structural biology, robotics,...
Anna Yershova, Swati Jain, Steven M. LaValle, Juli...
Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning ...