A Perspective on Open Challenges in Deformable Object Manipulation

2026-02-26

AI Summary

Manipulating deformable objects presents significant challenges due to occlusion, task generalization, and real-time control requirements. This work provides a systematic review of recent advances in this domain and proposes a high-level decision-making framework integrating graph neural networks. The approach unifies multimodal methodologies—including multi-camera active vision, tactile sensing, physics-informed reinforcement learning, differentiable simulation, and generative neural networks—to enhance manipulation capabilities. The study advocates for the establishment of standardized datasets and task benchmarks to facilitate effective sim-to-real transfer. By offering a cohesive technical pathway toward efficient, precise, and scalable deformable object manipulation, this research substantially improves the practicality of robotic systems in unstructured environments such as healthcare and manufacturing.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Multi-modal Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Deformable object manipulation (DOM) represents a critical challenge in robotics, with applications spanning healthcare, manufacturing, food processing, and beyond. Unlike rigid objects, deformable objects exhibit infinite dimensionality, dynamic shape changes, and complex interactions with their environment, posing significant hurdles for perception, modeling, and control. This paper reviews the state of the art in DOM, focusing on key challenges such as occlusion handling, task generalization, and scalable, real-time solutions. It highlights advancements in multimodal perception systems, including the integration of multi-camera setups, active vision, and tactile sensing, which collectively address occlusion and improve adaptability in unstructured environments. Cutting-edge developments in physically informed reinforcement learning (RL) and differentiable simulations are explored, showcasing their impact on efficiency, precision, and scalability. The review also emphasizes the potential of simulated expert demonstrations and generative neural networks to standardize task specifications and bridge the simulation-to-reality gap. Finally, future directions are proposed, including the adoption of graph neural networks for high-level decision-making and the creation of comprehensive datasets to enhance DOM's real-world applicability. By addressing these challenges, DOM research can pave the way for versatile robotic systems capable of handling diverse and dynamic tasks with deformable objects.

Problem

• Deformable Object Manipulation
• occlusion handling
• task generalization
• real-time solutions
• simulation-to-reality gap

Innovation

• deformable object manipulation
• multimodal perception
• physically informed reinforcement learning
• differentiable simulation
• generative neural networks