Learning Goal-oriented Bimanual Dough Rolling Using Dynamic Heterogeneous Graph Based on Human Demonstration

TL;DR

This paper introduces a dynamic heterogeneous graph-based model for goal-oriented soft object manipulation, specifically dough rolling, leveraging demonstrations to improve robot performance in both simulation and real-world settings.

Contribution

The paper presents a novel graph-based approach that unifies state representation and policy learning for soft object manipulation, incorporating demonstrations for guided learning.

Findings

The method outperforms baseline approaches in dough rolling tasks.

The approach achieves human-like manipulation behavior.

Ablation studies confirm the effectiveness of the dynamic heterogeneous graph model.

Abstract

Soft object manipulation poses significant challenges for robots, requiring effective techniques for state representation and manipulation policy learning. State representation involves capturing the dynamic changes in the environment, while manipulation policy learning focuses on establishing the relationship between robot actions and state transformations to achieve specific goals. To address these challenges, this research paper introduces a novel approach: a dynamic heterogeneous graph-based model for learning goal-oriented soft object manipulation policies. The proposed model utilizes graphs as a unified representation for both states and policy learning. By leveraging the dynamic graph, we can extract crucial information regarding object dynamics and manipulation policies. Furthermore, the model facilitates the integration of demonstrations, enabling guided policy learning. To evaluate the efficacy of our approach, we designed a dough rolling task and conducted experiments using both a differentiable simulator and a real-world humanoid robot. Additionally, several ablation studies were performed to analyze the effect of our method, demonstrating its superiority in achieving human-like behavior.