We present PRM-RL, a hierarchical method for long-range navigation task completion that
combines sampling-based path planning with reinforcement learning (RL). The RL agents …

We present a new optimization-based approach for robotic motion planning among
obstacles. Like CHOMP (Covariant Hamiltonian Optimization for Motion Planning), our …

We describe an integrated strategy for planning, perception, state estimation and action in
complex mobile manipulation domains based on planning in the belief space of probability …

Medical robotics is an interdisciplinary field that came into existence to improve medical
procedures utilizing robotics technology. Medical robotics range from minimally invasive …

Gaussian motion planning), a new approach to robot motion planning that takes into account
the sensors and the controller that will be used during execution of the robot· s path. LQG …

Long-range indoor navigation requires guiding robots with noisy sensors and controls
through cluttered environments along paths that span a variety of buildings. We achieve this …

This paper presents a strategy for planning robot motions in dynamic, uncertain
environments (DUEs). Successful and efficient robot operation in such environments …

In this paper we present feedback-based information roadmap (FIRM), a multi-query
approach for planning under uncertainty which is a belief-space variant of probabilistic …

When a mobile agent does not know its position perfectly, incorporating the predicted
uncertainty of future position estimates into the planning process can lead to substantially …

We propose to synthesize a control policy for a Markov decision process (MDP) such that the
resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a …