Regression with input-dependent noise: a Gaussian process treatment
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Learning and prediction of slip from visual information: Research Articles
Journal of Field Robotics - Special Issue on Space Robotics
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
Learning traversability models for autonomous mobile vehicles
Autonomous Robots
Online speed adaptation using supervised learning for high-speed, off-road autonomous driving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning to search: structured prediction techniques for imitation learning
Learning to search: structured prediction techniques for imitation learning
Analysis of solutions to the time-optimal planning and execution problem
Intelligent Service Robotics
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In this paper we address 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 learning technique is presented to generate a mobility representation where the maximum feasible speed is used as a criterion to classify the world. The inputs to the algorithm are terrain gradients derived from an elevation map and past observations of wheel slip. It is argued that such a representation can aid in path planning with improved selection of vehicle heading and velocity in off-road slopes. In addition, an information theoretic test is proposed to validate a chosen proprioceptive representation (such as slip) for mobility map generation. Results of mobility map generation and its benefits to path planning are shown.