Robot hands and the mechanics of manipulation
Robot hands and the mechanics of manipulation
Active learning for vision-based robot grasping
Machine Learning - Special issue on robot learning
Learning to Recognize and Grasp Objects
Machine Learning - Special issue on learning in autonomous robots
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Learning object-specific grasp affordance densities
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Learning grasp strategies with partial shape information
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
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When children learn to grasp a new object, they often know several possible grasping points from observing a parent's demonstration and subsequently learn better grasps by trial and error. From a machine learning point of view, this process is an active learning approach. In this paper, we present a new robot learning framework for reproducing this ability in robot grasping. For doing so, we chose a straightforward approach: first, the robot observes a few good grasps by demonstration and learns a value function for these grasps using Gaussian process regression. Subsequently, it chooses grasps which are optimal with respect to this value function using a mean-shift optimization approach, and tries them out on the real system. Upon every completed trial, the value function is updated, and in the following trials it is more likely to choose even better grasping points. This method exhibits fast learning due to the data-efficiency of the Gaussian process regression framework and the fact that the mean-shift method provides maxima of this cost function. Experiments were repeatedly carried out successfully on a real robot system. After less than sixty trials, our system has adapted its grasping policy to consistently exhibit successful grasps.