In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
Abstract— We present a novel approach to compute collisionfree paths for multiple robots subject to local coordination constraints. More specifically, given a set of robots, the...
Russell Gayle, William Moss, Ming C. Lin, Dinesh M...
— This paper addresses the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric l...
Sisir Karumanchi, Thomas Allen, Tim Bailey, Steve ...
We present a novel approach for interactive navigation in complex 3D synthetic environments using path planning. Our algorithm precomputes a global roadmap of the environment by u...
Brian Salomon, Maxim Garber, Ming C. Lin, Dinesh M...
In real-life temporal scenarios, uncertainty and preferences are often essential, coexisting aspects. We present a formalism where temporal constraints with both preferences and un...
Francesca Rossi, Kristen Brent Venable, Neil Yorke...