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Challenges and Outlook in Robotic Manipulation of Deformable Objects
Jihong Zhu, Andrea Cherubini, Claire Dune, David Navarro-Alarcon, Farshid Alambeigi, Dmitry Berenson, Fanny Ficuciello, Kensuke Harada, Jens Kober, Xiang Li, Jia Pan, Wenzhen Yuan, Michael Gienger
TL;DR
Deformable object manipulation remains an open robotics problem because deformation complicates sensing, modeling, and control while introducing many degrees of freedom. This article reviews advances across the main DOM components, identifies scientific and practical challenges, and uses a survey to position future directions. It highlights task-specific hardware, soft robotics, semantic planning, model uncertainty, and safety as central issues for further research.
Problem
Deformable object manipulation is more complex than rigid manipulation because deformation complicates sensing, modeling, planning, and control, despite its relevance to autonomous robotics.
Method
The paper reviews recent DOM work across gripper and robot design, sensing, modeling, planning, and control, then uses a 31-response survey to assess importance and research maturity.
Results
The paper identifies scientific and practical challenges and projects future research directions across DOM’s major robotics components.
Takeaways & Limitations
Progress in DOM requires coordinated advances in hardware, sensing, deformation modeling, planning, and control, including approaches for semantic goals and model uncertainty.
Abstract
from arXiv · showhide
Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Addressing DOM challenges demand breakthroughs in almost all aspects of robotics, namely hardware design, sensing, (deformation) modeling, planning, and control. In this article, we review recent advances and highlight the main challenges when considering deformation in each sub-field. A particular focus of our paper lies in the discussions of these challenges and proposing future directions of research.
I. INTRODUCTION
Deformable object manipulation extends robotics into applications where rigidity is inadequate, but introduces sensing, modeling, and high-dimensionality challenges. The paper surveys these challenges and uses expert ratings to identify promising future research directions.
- Rigidity is acceptable only when deformation can be neglected; many robotic tasks involve non-negligible deformation, from microsurgery to industrial assembly.
- Deformable object manipulation spans dressing assistance, cable harnessing, fruit harvesting, and surgical operations.
- DOM introduces challenges in sensing deformation, handling soft bodies’ many degrees of freedom, and modeling nonlinear deformation.
- The article emphasizes identifying scientific challenges and projecting future research directions rather than only reporting field progress or surveying one sub-area.
- The authors organize DOM around gripper and robot design, sensing, modeling, planning, and control.
- A survey of 31 respondents rated each component’s importance and research maturity, with promising directions defined as highly significant and immature.
A. Current capability
Current DOM capability relies on diverse, often task-specific hardware and must address dexterity, compliance, safety, under-actuation, and difficult deformation models. Soft robots offer opportunities for fragile-object manipulation but also introduce coupled modeling and control challenges.
- Hardware design: DOM commonly uses custom grippers, including cable-sliding grippers, towel clips, plastic-material tools, and soft hands for organ manipulation.
- Hardware design: Human-like dexterity and compliance could reduce reliance on task-specific grippers, but anthropomorphic design is not established as optimal for every DOM task.
- Hardware design: Hygiene, safety, biocompatibility, actuation, and restricted minimally invasive workspace can make task-specific surgical grippers more appropriate.
- Hardware design: Task-specific reasoning could let robots select suitable tools while retaining a standard gripper, supporting more complex tasks.
- Soft robotics: Soft robots and grippers offer compliant handling of fragile organs, food, biological samples, and fruits.
- Soft robotics: Using soft robots with deformable objects makes both robot and object under-actuated and difficult to model, motivating new modeling and control methods.
- Soft robotics: A key open question is whether modeling and control methods can transfer between soft robots and deformable objects toward a unified approach.
III. SENSING
DOM sensing combines vision, force, and tactile modalities to estimate deformable-object state, but selecting sensors and constructing meaningful representations remain open challenges.
- Vision usually captures global shape information, while force and tactile sensing provide local shape and contact information.
- Tactile sensing can estimate material stiffness from force-displacement behavior and high-resolution contact geometry.
- The division between global vision and local tactile sensing is not absolute, because either modality can support broader or more localized deformation estimates.
B. Challenges and outlook
DOM representations must remain useful despite noise and missing data while balancing dimensionality against accuracy. The paper identifies multimodal sensing and learning-based approaches as directions, but does not claim a complete representation solution.
- Fusing vision, force, and tactile sensing may help address deformable bodies’ high number of degrees of freedom.
- The authors acknowledge that they do not have a complete answer to what constitutes a good object-configuration representation.
- A useful object representation should be robust to noise and support configuration reconstruction when data are partially unavailable.Self-occlusion remains especially difficult for highly deformable objects such as clothes and bed sheets.
- Representation design must balance dimensionality and accuracy, with the trade-off depending on the task and often requiring trial and error.
- End-to-end reinforcement learning can map sensory data directly to robot actions, and one cloth-manipulation example used 20 human demonstrations.
IV. MODELING
DOM modeling links sensory information to robot motion through local or global models. Local models are efficient but limited in validity, whereas global models offer broader nonlinear approximation while retaining limited predictive power.
- Modeling distinguishes local and global models according to their role in control rather than only by deformation type.
- Local Jacobian-based models can be computed in real time with little data but require continuous updating during task execution.
- Global models based on finite elements or neural networks provide stronger representation power and can approximate highly nonlinear systems over larger validity ranges.
- Analytical and learned models have limited predictive power because they specialize to task classes or rely on incomplete training coverage of the high-dimensional state space.
- When learned models are unreliable, proposed approaches use model-validity reasoning and neural-network-based motion re-planning.
B. Challenges and outlook
DOM planning and learning must cope with deformation, limited model validity, and difficult simulation. Existing directions combine global planning with local control, use physical energy, or learn manipulation from demonstrations and reinforcement learning.
- Deformation modeling is difficult partly because few simulators can represent deformable behavior, limiting benchmarking and simulation-based research.Examples include Softgym, SOFA, and Chainqueen.
- Hierarchical models could combine a simple lower-level model with a complex nonlinear model to balance validity range and training-data requirements.
- Planning seeks valid robot-object configuration sequences and can combine global planning without physics simulation with local control.
- Energy-based planning provides an alternative for elastic objects, including dual-arm manipulation approaches.
- Learning from demonstration encodes trajectories and interaction forces, while reinforcement learning can address DOM without initial demonstrations.
B. Challenges and outlook
DOM planning must account for changing configurations, semantic task goals, and the usefulness of contacts rather than treating contact as something to avoid. These challenges motivate sensing, re-grasping, and learned goal representations.
- Planning contacts can constrain deformable-object configurations and simplify manipulation, unlike planners that generally avoid contact.Examples include cable-harness contact points and flat surfaces for folding clothes.
- Re-grasp planning and configuration tracking are crucial because releasing grasps can change the object's configuration during multi-arm tasks.Grasping appropriate points, such as convex clothing vertices, can improve stability and facilitate manipulation.
- Semantic planning must learn concepts such as folded or wrapped to recognize valid goals without manually specifying every deformable-object configuration.
A. Current capability
Current DOM systems use task-dependent controllers, feedback from sensed or modeled object states, and both model-based and model-free approaches. However, under-actuation, model inaccuracy, and lost contact still constrain autonomy and controllability.
- DOM controllers are selected according to task requirements, including data-driven model predictive control, fuzzy compensation with impedance control, and shape-based control.
- Global control models map sensor data directly to robot motion, whereas local models must be inverted to design the motion controller.These approaches have been applied in manufacturing tasks involving grasping, contact, active deformation, or separation.
- Feedback control is common in DOM, but model-based controllers must account for deformation models that may be inaccurate or wrong.Object state is obtained from deformation-model output and sensors, and output and state may differ in representation and dimension.
- Model-free control avoids deformation-model parameters or structure, but requires efficient data use and policy generalization.Combining offline and online learning is proposed to address these challenges.
- When multiple object features are controlled in parallel, fewer control inputs than error outputs create under-actuation and possible feature conflicts.Controllers must resolve conflicts or decouple feature control sequentially to preserve controllability.
- Deformation can break end-effector contact, while existing systems often need human assistance to initiate or re-establish contact.Automatic grasping or touching after contact loss is identified as a route toward greater autonomy.
VII. PRACTICAL APPLICATIONS
DOM has practical applications across domestic assistance, healthcare, food handling, and marine robotics, but deployment remains limited by safety, robustness, and real-world complexity. The field spans both emerging autonomous systems and established tele-operated or industrial platforms.
- Application-specific DOM challenges are discussed as a complement to the paper's scientific treatment of the field.
- Automatic laundry: Laundry folding remains largely a laboratory product limited to structured environments and certain clothing types, making commercialization substantially difficult.A laundry-folding robot prototype was followed by its company's bankruptcy amid development funding and performance difficulties.
- Assistive dressing: Robot-assisted dressing includes simulation-based and imitation-learning approaches for shoes, shirts, and pants, but technical and societal challenges remain.
- Surgical robotics: Surgical soft-tissue manipulation is mainly tele-operated with visual feedback, while autonomous manipulation still requires substantial hardware and software development.Safety is identified as the biggest concern for autonomous solutions; intrinsic compliance may enhance safety.
- Food production & Retail: Food and retail operations involve deformable products from production through sales, including harvesting, processing, packaging, warehousing, ordering, and restocking.Fragile products can be damaged during interaction, reducing their value and shelf life; more complex operations are often still handled by humans.
- Marine robotics: Marine DOM applications include detailed underwater tasks in biology, sedimentology, and archaeology, as well as tethered-robot umbilical modeling and control.Negative-buoyancy cable can be modeled in real time as a simple catenary and tracked for tethered ROV control.
VIII. SUMMARY AND KEY MESSAGES
DOM challenges fundamental assumptions in robotics and require advances across hardware, sensing, modeling, planning, and control. The paper highlights cross-disciplinary research directions and applications spanning soft robots, sensing, physical models, contact-aware planning, and under-actuated control.
- DOM breaks assumptions of rigidity, known dynamics, and low-dimensional state spaces, requiring breakthroughs across robotic hardware and software.
- Interactions between soft robots and deformable objects raise transferability questions and may motivate unified modeling and control approaches.
- Sensing must process information from multiple modes and use machine learning to generate meaningful representations of deformation.
- No single deformation model is universally best; physical models remain important for studying interactions alongside data-driven approaches.
- Planning should address missing high-level semantic reasoning and can deliberately use contact rather than treating contact solely as a collision to avoid.
- Under-actuation arises from deformable bodies’ high DoF, while future controllers must detect and react to contact loss.