We describe a novel integration of Planning with Probabilistic State Estimation and Execution resulting in a unified representational and computational framework based on declarat...
Conor McGann, Frederic Py, Kanna Rajan, John Ryan,...
Abstract— We present an efficient algorithm for complete motion planning that combines approximate cell decomposition (ACD) with probabilistic roadmaps (PRM). Our approach uses ...
— Free-space dilation is an effective approach for narrow passage sampling, a well-recognized difficulty in probabilistic roadmap (PRM) planning. Key to this approach are method...
— 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...
This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environ...
Stefanie Tellex, Thomas Kollar, Steven Dickerson, ...