Attribute-Based Robotic Grasping with One-Grasp Adaptation

no code implementations • 6 Apr 2021 • Yang Yang, YuanHao Liu, Hengyue Liang, Xibai Lou, Changhyun Choi

In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability.

Attribute Object +1

Collision-Aware Target-Driven Object Grasping in Constrained Environments

no code implementations • 1 Apr 2021 • Xibai Lou, Yang Yang, Changhyun Choi

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.

Object Robotic Grasping

Learning to Generate 6-DoF Grasp Poses with Reachability Awareness

no code implementations • 14 Oct 2019 • Xibai Lou, Yang Yang, Changhyun Choi

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.

Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures

no code implementations • 9 Oct 2019 • Hengyue Liang, Xibai Lou, Yang Yang, Changhyun Choi

This Slide-to-Wall grasping task assumes no prior knowledge except the partial observation of a target object.

Object Robotic Grasping +1