A Perspective on Open Challenges in Deformable Object Manipulation

TL;DR

Key challenges such as occlusion handling, task generalization, and scalable, real-time solutions are addressed, focusing on key challenges such as occlusion handling, task generalization, and scalable, real-time solutions.

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.

Figures (7)

• Figure 1: Illustration of cloth modeling using a mass-spring system: a triangular mesh structure representing the cloth is depicted, with a magnified view showing the individual mass points and spring connections, highlighting the deformation mechanics and the use of Hooke's law (bottom centre).
• Figure 2: The undeformed cube (left) represents the reference configuration, while the deformed cube (right) illustrates the effect of compressive forces acting along the horizontal sides (grey arrows). The deformation is described by the deformation gradient ($\mathbf{F}$), where $\mathbf{F} = \frac{\partial \mathbf{x}}{\partial \mathbf{X}}$, mapping the reference configuration ($\mathbf{X}$) to the deformed configuration ($\mathbf{x}$). The strain in the material is captured by the strain tensor $\boldsymbol{\varepsilon} = \frac{1}{2} \left( \nabla \mathbf{u} + (\nabla \mathbf{u})^\top \right)$, with $\varepsilon_{xx} < 0$ indicating compression along the horizontal axis. The stress tensor ($\boldsymbol{\sigma}$) satisfies the equilibrium condition $\nabla \cdot \boldsymbol{\sigma} = 0$ under static loading, and the material behavior follows Hooke’s law: $\boldsymbol{\sigma} = \mathbf{C} : \boldsymbol{\varepsilon}$. The top and bottom faces remain undeformed due to fixed boundary conditions ($u_y = 0$).
• Figure 3: Shooting control diagram demonstrating forward dynamics and optimization. The constrained variables are in the action and control space (dashed rectangle). Diagram is based on shiach2024
• Figure 4: State trajectory exploration: The constrained variables lie within the state space (dashed rectangle), with actions determined based on state trajectories and inverse dynamics. This is a common framework in Model-Predictive-Control, a thorough explanation can be found here tedrake_underactuated
• Figure 5: A basic closed-loop control framework based on visual features. A thorough description can be found here spong2005