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This paper reviews the current state of deformable object manipulation (DOM) in robotics, highlighting challenges in perception, modeling, and control due to the infinite dimensionality and dynamic nature of deformable objects. It surveys advancements in multimodal perception, physically informed reinforcement learning, and differentiable simulations for improving efficiency and scalability in DOM tasks. The review emphasizes the potential of simulated expert demonstrations and graph neural networks for task specification and high-level decision-making, ultimately aiming to bridge the simulation-to-reality gap.
Overcoming the perception, modeling, and control challenges of deformable object manipulation could unlock versatile robotic systems across healthcare, manufacturing, and food processing.
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.
