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Found 7 papers, 1 papers with code
Deep Dexterous Grasping of Novel Objects from a Single View
First, we present a simulator for generating and testing dexterous grasps.
Learning better generative models for dexterous, single-view grasping of novel objects
This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single view-point.
Generative grasp synthesis from demonstration using parametric mixtures
We present a parametric formulation for learning generative models for grasp synthesis from a demonstration.
Robotics
Hypothesis-based Belief Planning for Dexterous Grasping
However, this planning approach was tried successfully only on simplified control problems.
Towards Deep Compositional Networks
We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function.
Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-Manifold Shapes
We represent the topological relationship between shape components using graphs, which are aggregated to construct a hierarchical graph structure for the shape vocabulary.
A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model
A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP).
