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Found 8 papers, 1 papers with code
A Deep Learning Approach to Grasping the Invisible
The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning.
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures
This Slide-to-Wall grasping task assumes no prior knowledge except the partial observation of a target object.
Learning to Generate 6-DoF Grasp Poses with Reachability Awareness
Motivated by the stringent requirements of unstructured real-world where a plethora of unknown objects reside in arbitrary locations of the surface, we propose a voxel-based deep 3D Convolutional Neural Network (3D CNN) that generates feasible 6-DoF grasp poses in unrestricted workspace with reachability awareness.
Collision-Aware Target-Driven Object Grasping in Constrained Environments
Grasping a novel target object in constrained environments (e. g., walls, bins, and shelves) requires intensive reasoning about grasp pose reachability to avoid collisions with the surrounding structures.
KINet: Unsupervised Forward Models for Robotic Pushing Manipulation
Using visual observations, our model learns to associate objects with keypoint coordinates and discovers a graph representation of the system as a set of keypoint embeddings and their relations.
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach
Instance segmentation with unseen objects is a challenging problem in unstructured environments.
SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics
Our SlotGNN, a novel unsupervised graph-based dynamics model, predicts the future state of multi-object scenes.
