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An In Depth View of Saliency

Presented at the 24th British Machine Vision Conference (BMVC 2013), 9-13 September 2013, Bristol, UK.
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Affordance Prediction via Learned Object Attributes

We present a novel method for learning and predicting the affordances of an object based on its physical and visual attributes. Affordance prediction is a key task in autonomous robot learning, as it…

Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network

This work is the first to directly plan high quality multifingered grasps in configuration space using a deep neural network without the need of an external planner and shows that the planning method outperforms existing planning methods for neural networks.
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Decoupling behavior, perception, and control for autonomous learning of affordances

This work demonstrates the approach using a PR2 robot that explores different combinations of controller, behavior primitive, and proxy to perform a push or pull positioning behavior on a selection of household objects, learning which methods best work for each object.
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Movie genre classification via scene categorization

This paper presents a method for movie genre categorization of movie trailers, based on scene categorization, and demonstrates that exploiting scene structures improves film genre classification using only low-level visual features.
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Stabilizing novel objects by learning to predict tactile slip

This work explores the generalization capabilities of well known supervised learning methods, using random forest classifiers to create generalizable slip predictors in the feedback loop of an object stabilization controller and shows that the controller can successfully stabilize previously unknown objects by predicting and counteracting slip events.
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Learning contact locations for pushing and orienting unknown objects

With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class.
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Learning Continuous 3D Reconstructions for Geometrically Aware Grasping

This work proposes to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system, and leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization.
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Modeling Grasp Type Improves Learning-Based Grasp Planning

This paper proposes a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real time and shows the benefit of learning a prior over grasp configurations to improve grasp inference with a learned classifier.
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Learning robot in-hand manipulation with tactile features

This approach successfully acquires a tactile manipulation skill using a passively compliant hand and it is shown that the learned tactile skill generalizes to novel objects.
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