Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4

Co-manipulation of soft-materials estimating deformation from depth images

Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect measure by calculating the human-robot relative distance. In this paper, we develop a data-driven model to estimate the deformation state of the material from a depth image through a Convolutional Neural Network (CNN). First, we define the deformation state of the material as the relative roto-translation from the current robot pose and a human grasping position. The model estimates the current deformation state through a Convolutional Neural Network, specifically a DenseNet-121 pretrained on ImageNet.The delta between the current and the desired deformation state is fed to the robot controller that outputs twist commands. The paper describes the developed approach to acquire, preprocess the dataset and train the model. The model is compared with the current state-of-the-art method based on a skeletal tracker from cameras. Results show that our approach achieves better performances and avoids the various drawbacks caused by using a skeletal tracker.Finally, we also studied the model performance according to different architectures and dataset dimensions to minimize the time required for dataset acquisitionComment: Pre-print, submitted to Journal of Intelligent Manufacturin

Learning to Singulate Layers of Cloth using Tactile Feedback

Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on vision sensors alone, which may pose challenges for fine-grained manipulation tasks such as grasping a desired number of cloth layers from a stack of cloth. In this paper, we propose to use tactile sensing for cloth manipulation; we attach a tactile sensor (ReSkin) to one of the two fingertips of a Franka robot and train a classifier to determine whether the robot is grasping a specific number of cloth layers. During test-time experiments, the robot uses this classifier as part of its policy to grasp one or two cloth layers using tactile feedback to determine suitable grasping points. Experimental results over 180 physical trials suggest that the proposed method outperforms baselines that do not use tactile feedback and has better generalization to unseen cloth compared to methods that use image classifiers. Code, data, and videos are available at https://sites.google.com/view/reskin-cloth.Comment: IROS 2022. See https://sites.google.com/view/reskin-cloth for supplementary materia

Robotic Fabric Flattening with Wrinkle Direction Detection

Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc. Perception is considered one of the major challenges in DOM due to the complex dynamics and high degree of freedom of deformable objects. In this paper, we develop a novel image-processing algorithm based on Gabor filters to extract useful features from cloth, and based on this, devise a strategy for cloth flattening tasks. We evaluate the overall framework experimentally, and compare it with three human operators. The results show that our algorithm can determine the direction of wrinkles on the cloth accurately in the simulation as well as the real robot experiments. Besides, the robot executing the flattening tasks using the dewrinkling strategy given by our algorithm achieves satisfying performance compared to other baseline methods. The experiment video is available on https://sites.google.com/view/robotic-fabric-flattening/hom

Art and Design Practices as a Driver for Deformable Controls, Textures and Screen Interactions

In this thesis, we demonstrate the innovative uses of deformable interfaces to help de-velop future digital art and design interactions. The great benefits of advancing digital art can often come at a cost of tactile feeling and physical expression, while traditional methods celebrate the diverse sets of physical tools and materials. We identified these sets of tools and materials to inform the development of new art and design interfaces that offer rich physical mediums for digital artist and designers. In order to bring forth these unique inter-actions, we draw on the latest advances in deformable interface technology. Therefore, our research contributes a set of understandings about how deformable interfaces can be har-nessed for art and design interfaces. We identify and discuss the following contributions: insights into tangible and digital practices of artists and designers; prototypes to probe the benefits and possibilities of deformable displays and materials in support of digital-physical art and design, user-centred evaluations of these prototypes to inform future developments, and broader insights into the deformable interface research.Each chapter of this thesis investigates a specific element of art and design, alongside an aspect of deformable interfaces resulting in a new prototype. We begin the thesis by studying the use of physical actuation to simulate artist tools in deformable surfaces. In this chapter, our evaluations highlight the merits of improved user experiences and insights into eyes-free interactions. We then turn to explore deformable textures. Driven by the tactile feeling of mixing paints, we present a gel-based interface that is capable of simulating the feeling of paints on the back of mobile devices. Our evaluations showed how artists endorsed the interactions and held potential for digital oil painting.Our final chapter presents research conducted with digital designers. We explore their colour picking processes and developed a digital version of physical swatches using a mod-ular screen system. This use of tangible proxies in digital-based processes brought a level of playfulness and held potential to support collaborative workflows across disciplines. To conclude, we share how our outcomes from these studies could help shape the broader space of art and design interactions and deformable interface research. We suggest future work and directions based on our findings