A Perspective on Open Challenges in Deformable Object Manipulation

TL;DR

This paper reviews the current challenges and recent advancements in deformable object manipulation in robotics, emphasizing perception, modeling, control, and future research directions to enhance real-world applicability.

Contribution

It provides a comprehensive overview of key challenges, recent technological progress, and future research directions in deformable object manipulation for robotics.

Findings

Advancements in multimodal perception systems improve occlusion handling.

Physically informed RL and differentiable simulations enhance efficiency and scalability.

Potential of neural networks and datasets to bridge simulation and real-world applications.

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.