Robotic Manipulation Of Deformable Objects Research Articles

Deformable Object Manipulation in Caregiving Scenarios: A Review

This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laboratory successes have been achieved, real-world caregiving applications lag behind. Enhancing safety, speed, generalisation, and human compatibility is crucial for adoption. The review synthesises critical technologies, capabilities, and limitations, while also pointing to open challenges in deformable object manipulation for robotic caregiving. It provides a comprehensive reference for researchers tackling this socially valuable domain. In conclusion, multi-disciplinary innovations combining analytical and data-driven methods are needed to advance real-world robot performance and safety in deformable object manipulation for patient care.

Interaction Control of a Robotic Manipulator With the Surface of Deformable Object

Robotic manipulation of deformable objects has drawn the attention of researchers over the past few years and is associated with a large spectrum of new application perspectives. In this article, we present an efficient integrated motion planning framework to effectively and accurately control a robotic manipulator executing interactive tasks on the surface of a deformable object. The proposed interactive motion planning framework is based on a mesh representation of the object, integrating three efficient preprocessing algorithmic steps, including visual object segmentation, finite element method deformation tracking, and local mesh parameterization. The use of barycentric coordinates, defined on the mesh triangles, enables the establishment of bijective transformations between the deformable part of an object surface and its planar (static and dynamic) parameterized mapping. By merging these spatial transformations with the preprocessing steps, in combination with an active stiffness scheme for robot manipulator control, we are able to achieve accurate and reactive motion planning of interactive trajectories, even under large and persistent visual occlusions (such as due to the presence of the robot in the visual scene). An extensive experimental evaluation study is presented, involving a robotic manipulator in interaction with a hemispherical model of controllable periodic active deformation, which permits precise ground truth derivation. Motion planning accuracy is evaluated in comparison with our previous direct vision-based approach, showing clearly superior performance of the proposed approach under all experimental conditions. The performance of the proposed framework is also further highlighted in tasks involving physical point tracking, interactive programming by human demonstration, as well as contact force regulation.

Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement learning has shown promising results in learning complex visuomotor policies, especially in simulation environments where many samples can be collected at low cost. A core challenge is learning policies in simulation that can be deployed in the real world, thereby overcoming the sim-to-real gap. In this letter, we bridge the visual sim-to-real gap with an image-based reinforcement learning pipeline based on pixel-level domain adaptation and demonstrate its effectiveness on an image-based task in deformable object manipulation. We choose a tissue retraction task because of its importance in clinical reality of precise cancer surgery. After training in simulation on domain-translated images, our policy requires no retraining to perform tissue retraction with a 50% success rate on the real robotic system using raw RGB images. Furthermore, our sim-to-real transfer method makes no assumptions on the task itself and requires no paired images. This letter introduces the first successful application of visual sim-to-real transfer for robotic manipulation of deformable objects in the surgical field, which represents a notable step towards the clinical translation of cognitive surgical robotics.

Challenges and Outlook in Robotic Manipulation of Deformable Objects

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 health-care 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 demands breakthroughs in almost all aspects of robotics: 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 subfield. A particular focus of our article lies in the discussions of these challenges and proposing future directions of research.

Automated Stability Testing of Elastic Rods With Helical Centerlines Using a Robotic System

Experimental analysis of the mechanics of a deformable object, and particularly its stability, requires repetitive testing and, depending on the complexity of the object’s shape, a testing setup that can manipulate many degrees of freedom at the object’s boundary. Motivated by recent advancements in robotic manipulation of deformable objects, this letter addresses these challenges by constructing a method for automated stability testing of a slender elastic rod — a canonical example of a deformable object — using a robotic system. We focus on rod configurations with helical centerlines since the stability of a helical rod can be described using only three parameters, but experimentally determining the stability requires manipulation of both the position and orientation at one end of the rod, which is not possible using traditional experimental methods that only actuate a limited number of degrees of freedom. Using a recent geometric characterization of stability for helical rods, we construct and implement a manipulation scheme to explore the space of stable helices, and we use a vision system to detect the onset of instabilities within this space. The experimental results obtained by our automated testing system show good agreement with numerical simulations of elastic rods in helical configurations. The methods described in this letter lay the groundwork for automation to grow within the field of experimental mechanics.

Robotic Manipulation of Deformable Cells for Orientation Control

Robotic manipulation of deformable objects has been a classic topic in robotics. Compared to synthetic deformable objects such as rubber balls and clothes, biological cells are highly deformable and more prone to damage. This article presents robotic manipulation of deformable cells for orientation control (both out-of-plane and in-plane), which is required in both clinical (e.g., in vitro fertilization) and biomedical (e.g., clone) applications. Compared to manual cell orientation control based on empirical experience, the robotic approach, based on modeling and path planning, effectively rotates a cell, while consistently maintaining minimal cell deformation to avoid cell damage. A force model is established to determine the minimal force applied by the micropipette to rotate a spherical or, more generally, ellipsoidal oocyte. The force information is translated into indentation through a contact mechanics model, and the manipulation path of the micropipette is formed by connecting the indentation positions on the oocyte. An optimal controller is designed to compensate for the variations of mechanical properties across oocytes. The polar body of an oocyte is detected by deep neural networks with robustness to shape and size differences. In experiments, the system achieved an accuracy of 97.6% in polar body detection and an accuracy of 0.7 $^{\circ }$ in oocyte orientation control with maximum oocyte deformation of 2.70 $\mu$ m throughout the orientation control process.

Model-Driven Feedforward Prediction for Manipulation of Deformable Objects

Robotic manipulation of deformable objects is a difficult problem especially because of the complexity of the many different ways an object can deform. Searching such a high-dimensional state space makes it difficult to recognize, track, and manipulate deformable objects. In this paper, we introduce a predictive, model-driven approach to address this challenge, using a precomputed, simulated database of deformable object models. Mesh models of common deformable garments are simulated with the garments picked up in multiple different poses under gravity, and stored in a database for fast and efficient retrieval. To validate this approach, we developed a comprehensive pipeline for manipulating clothing as in a typical laundry task. First, the database is used for category and the pose estimation is used for a garment in an arbitrary position. A fully featured 3-D model of the garment is constructed in real time, and volumetric features are then used to obtain the most similar model in the database to predict the object category and pose. Second, the database can significantly benefit the manipulation of deformable objects via nonrigid registration, providing accurate correspondences between the reconstructed object model and the database models. Third, the accurate model simulation can also be used to optimize the trajectories for the manipulation of deformable objects, such as the folding of garments. Extensive experimental results are shown for the above tasks using a variety of different clothings. Note to Practitioners —This paper provides an open source, extensible, 3-D database for dissemination to the robotics and graphics communities. Model-driven methods are proliferating, and they need to be applied, tested, and validated in real environments. A key idea we have exploited is to have an innovative and novel use of simulation. This database will serve as infrastructure for developing advanced robotic machine learning algorithms. We want to address this machine learning idea ourselves, but we expect the dissemination of the database to other researchers with different agendas and task applications, which will bring wide progress in this area. Our proposed methods, as mentioned earlier, can be easily applied to interrelated areas. One example is that the 3-D shape-based matching algorithm can be used for other objects, such as bottles, papers, and food. After integrating with other robotic systems, the use of the robot can be easily extended to other tasks, such as making food, cleaning room, and fetching objects, to assist our daily life.