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Learning Task-Oriented Grasping from Human Activity Datasets
We develop a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape.
Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images
The qualitative experiments show results of pose and shape estimation of objects held by a hand "in the wild".
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores.
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