2 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
Interactive Robotic Grasping with Attribute-Guided Disambiguation
Yang Yang, Xibai Lou, Changhyun Choi
Interactive robotic grasping using natural language is one of the most fundamental tasks in human-robot interaction. However, language can be a source of ambiguity, particularly wh…
Learning Object Relations with Graph Neural Networks for Target-Driven Grasping in Dense Clutter
Xibai Lou, Yang Yang, Changhyun Choi
Robots in the real world frequently come across identical objects in dense clutter. When evaluating grasp poses in these scenarios, a target-driven grasping system requires knowled…
Attribute-Based Robotic Grasping with One-Grasp Adaptation
Yang Yang, Yuanhao Liu, Hengyue Liang +2
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in cl…
Collision-Aware Target-Driven Object Grasping in Constrained Environments
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…
Learning to Generate 6-DoF Grasp Poses with Reachability Awareness
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 3…
A Deep Learning Approach to Grasping the Invisible
Yang Yang, Hengyue Liang, Changhyun Choi
We study an emerging problem named "grasping the invisible" in robotic manipulation, in which a robot is tasked to grasp an initially invisible target object via a sequence of push…