Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging

by   Yuhong Deng, et al.
Tsinghua University

Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus on designing an expert system for each specific task by model-based or data-driven approaches and the application scenarios are therefore limited. 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. However, transferring from simulation to reality is difficult due to the limitation of the end-to-end CNN architecture. To address these challenges, we design a local GNN (Graph Neural Network) based learning method, which utilizes two representation graphs to encode keypoints detected from images. Self-attention is applied for graph updating and cross-attention is applied for generating manipulation actions. Extensive experiments have been conducted to demonstrate that our framework is effective in multiple 1-D (rope, rope ring) and 2-D (cloth) rearranging tasks in simulation and can be easily transferred to a real robot by fine-tuning a keypoint detector.


page 1

page 3

page 4

page 5

page 6

page 7


Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task

Rearranging deformable objects is a long-standing challenge in robotic m...

Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics

Vision-based deformable object manipulation is a challenging problem in ...

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

We have seen much recent progress in rigid object manipulation, but inte...

Efficient and Interpretable Robot Manipulation with Graph Neural Networks

Many manipulation tasks can be naturally cast as a sequence of spatial r...

Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objects

This paper tackles the task of goal-conditioned dynamic manipulation of ...

Learning Deformable Object Manipulation from Expert Demonstrations

We present a novel Learning from Demonstration (LfD) method, Deformable ...

Please sign up or login with your details

Forgot password? Click here to reset