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Background

  • Background The paper discusses the significance and application range of robotic manipulation of deformable objects (DOM) in various fields such as industrial service, medical care, food processing, and elderly care. Compared to the manipulation of rigid objects, DOM faces significant challenges in perception, modeling, and manipulation due to the infinite dimensionality of the state space of deformable objects (DOs) and the complexity of their dynamics.

  • Existing Work Despite the advancements in computer graphics and machine learning enabling data-driven techniques for DOM, existing reviews do not comprehensively cover all aspects of DOM or adequately summarize data-driven approaches. These limitations have led to a more comprehensive overview of over 150 relevant studies (mainly data-driven approaches) on the latest developments, unresolved challenges, and new frontiers in perception, modeling, and manipulation.

Core Contributions

  • A survey on the field of DOM

    • Challenge 1: Multi-modality and efficiency in perception Accurate and efficient multi-modal perception is crucial for DOM, and finding an accurate and efficient way to represent DOs remains an open challenge. The paper summarizes modules in terms of visual, tactile, and multimodal perception and discusses multimodal manipulation datasets and tactile simulators as two key elements for the future of DOM perception.

    • Challenge 2: Data-driven methods for modeling and manipulation While previous reviews focus on analytical approaches, this paper highlights data-driven methods for modeling DOs using Graph Neural Networks (GNNs). The review also covers more advanced Reinforcement Learning (RL) and Imitation Learning (IL) methods, discusses current open challenges, and points out future research directions for perception, modeling, and manipulation.

Implementation and Deployment

Regarding implementation and deployment, the paper does not delve into practical application analyses or demonstrations of the methods or concepts proposed in the review. The evaluation results are primarily conducted by sorting and comparing the development of various DOM methods in the existing literature, emphasizing the potential of combining data-driven and analytical approaches. There are no detailed quantitative comparisons with other relevant works provided, but the paper does discuss the current challenges and new directions for future research extensively.

Summary

This survey compiles recent advances, challenges, and new frontiers in the field of robotic manipulation of deformable objects (DOM). It notably emphasizes the initial progress of Large Language Models (LLMs) in robotic manipulation and points out important directions for further research in this area. While the review covers a broad range of literature and identifies future research directions, actual deployment examples and quantitative evaluations are limited.