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Found 6 papers, 3 papers with code
Learning visual-based deformable object rearrangement with local graph neural networks
Our method reaches much higher success rates on a variety of deformable rearrangement tasks (96. 3% on average) than state-of-the-art method in simulation experiments.
Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging
Some research has been attempting to design a general framework to obtain more advanced manipulation capabilities for deformable rearranging tasks, with lots of progress achieved in simulation.
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects.
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably.
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Foldformer can complete multi-step cloth manipulation tasks even when configurations of the cloth (e. g., size and pose) vary from configurations in the general demonstrations.
MQA: Answering the Question via Robotic Manipulation
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given question.
