Tucker Hermans

We propose a novel method which incorporates depth measurements into the computation of visual saliency. Expand

This paper presents a method for movie genre categorization of movie trailers, based on scene categorization. Expand

We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. Expand

A novel behavior representation is introduced that permits a robot to systematically explore the best methods by which to successfully execute an affordance-based behavior for a particular object. Expand

We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape, regardless of whether they belong to a previously encountered object class. Expand

We propose a reinforcement learning method to learn a tactile skill on a compliant, under-actuated robot hand that generalizes to novel objects. Expand

We explore the generalization capabilities of well known supervised learning methods, using random forest classifiers to create generalizable slip predictors. Expand

We present an active touch strategy to efficiently reduce the surface geometry uncertainty for tactile object surface modeling by leveraging a probabilistic representation of object surface. Expand

We explore learning a robust model that maps tactile sensor signals to force via neural networks. Expand