2 citations · 7 across the 9 of their papers we have counts for
11 papers
IOSG: Image-driven Object Searching and Grasping
Houjian Yu, Xibai Lou, Yang Yang +1
When robots retrieve specific objects from cluttered scenes, such as home and warehouse environments, the target objects are often partially occluded or completely hidden. Robots a…
Active Mass Distribution Estimation from Tactile Feedback
Jiacheng Yuan, Changhyun Choi, Ellad B. Tadmor +1
In this work, we present a method to estimate the mass distribution of a rigid object through robotic interactions and tactile feedback. This is a challenging problem because of th…
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach
Houjian Yu, Changhyun Choi
Instance segmentation with unseen objects is a challenging problem in unstructured environments. To solve this problem, we propose a robot learning approach to actively interact wi…
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…
KINet: Unsupervised Forward Models for Robotic Pushing Manipulation
Alireza Rezazadeh, Changhyun Choi
Object-centric representation is an essential abstraction for forward prediction. Most existing forward models learn this representation through extensive supervision (e.g., object…
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…
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures
Hengyue Liang, Xibai Lou, Yang Yang +1
This paper introduces a challenging object grasping task and proposes a self-supervised learning approach. The goal of the task is to grasp an object which is not feasible with a s…
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…