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Research

SlotGNN
SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics [ICRA'24]
KiNet
KINet: Unsupervised Forward Models for Robotic Pushing Manipulation [RAL'23]
Yang et al., ICRA'22
Interactive Robotic Grasping with Attribute-Guided Disambiguation [ICRA'22]
Lou et al., ICRA'22
Learning Object Relations with Graph Neural Networks for Target-Driven Grasping in Dense Clutter [ICRA'22]
Yang et al., ICRA'21
Attribute-Based Robotic Grasping with One-Grasp Adaptation [ICRA'21]
Lou et al., ICRA'21
Collision-Aware Target-Driven Object Grasping in Constrained Environments [ICRA'21]
Liang et al., ICRA'21
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures [ICRA'21]
Yang et al., RA-L'20
A Deep Learning Approach to Grasping the Invisible [RA-L'20]
Lou et al., ICRA'20
Learning to Generate 6-DoF Grasp Poses with Reachability Awareness [ICRA'20]

 

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