Despite the recent advances in planning with MDPs, the problem of generating good policies is still hard. This paper describes a way to generate policies in MDPs by (1) determiniz...
The results of the latest International Probabilistic Planning Competition (IPPC-2008) indicate that the presence of dead ends, states with no trajectory to the goal, makes MDPs h...
— We introduce a hierarchical variant of the probabilistic roadmap method for motion planning. By recursively refining an initially sparse sampling in neighborhoods of the C-obs...
— Reaching is a critical task for humanoid robots, requiring the application of state-of-the-art algorithms for motion planning and inverse kinematics. Practical algorithms for s...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...