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 pre-computed, 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 pose estimation for a garment in an arbitrary position. A fully featured 3D 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 non-rigid 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 manipulation of deformable objects, such as the folding of garments. Extensive experimental results are shown for the tasks above using a variety of different clothing.
Carnegie Mellon University（卡内基梅隆大学）；UC San Diego（加州大学圣地亚哥分校）；MIT（麻省理工学院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorithms for deformable object manipulation. However, such gradient-based trajectory optimization typically requires access to the full simulator states and can only solve short-horizon, single-skill tasks due to local optima. In this work, we propose a novel framework, named DiffSkil…
Existing shape estimation methods for deformable object manipulation suffer from the drawbacks of being off-line, model dependent, noise-sensitive or occlusion-sensitive, and thus are not appropriate for manipulation tasks requiring high precision. In this paper, we present a real-time shape estimation approach for autonomous robotic manipulation of 3D deformable objects. Our method fulfills all the requirements necessary for the high-quality deformable object manipulation in terms of being real-time, model-free and robust to noise and occlusion. These advantages are accomplished using a joint…
ArtiMinds Robotics（ArtiMinds机器人公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
This paper introduces an efficient procedure to localize user-defined points on the surface of deformable objects and track their positions in 3D space over time. To cope with a deformable object's infinite number of DOF, we propose a discretized deformation field, which is estimated during runtime using a multi-step non-linear solver pipeline. The resulting high-dimensional energy minimization problem describes the deviation between an offline-defined reference model and a pre-processed camera image. An additional regularization term allows for assumptions about the object's hidden areas and …
We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). With our built multi-view capture system, it captures the deformable interactions with multiple perspectives, various object shapes, and diverse interactive forms. Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, the visual feature…
SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)
Tuojing Intelligence（拓景智能）；Tsinghua University（清华大学）；King’s College London（伦敦国王学院）；Southeast University（东南大学）；Stevens Institute of Technology（史蒂文斯理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Manchester（曼彻斯特大学）；Simple AI（简单人工智能公司）；Imperial College London（伦敦帝国学院）；Carnegie Mellon University（卡内基梅隆大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；The University of Hong Kong（香港大学）
Cognitive Robotics, TU Delft（代尔夫特理工大学认知机器人）；Honda Research Institute, Europe（本田研究院欧洲）；LIRMM - Université de Montpellier CNRS（蒙彼利埃大学 CNRS LIRMM）；COSMER laboratory EA 7398, Universié de Toulon（土伦大学 COSMER 实验室 EA 7398）；The Hong Kong Polytechnic University（香港理工大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Michigan（密歇根大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）；Osaka University（大阪大学）；National Institute of AIST（产业技术综合研究所）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
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 adv…
Stanford University（斯坦福大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；NVIDIA（英伟达）；Caltech（加州理工学院）
Manipulating volumetric deformable objects in the real world, like plush toys and pizza dough, bring substantial challenges due to infinite shape variations, non-rigid motions, and partial observability. We introduce ACID, an action-conditional visual dynamics model for volumetric deformable objects based on structured implicit neural representations. ACID integrates two new techniques: implicit representations for action-conditional dynamics and geodesics-based contrastive learning. To represent deformable dynamics from partial RGB-D observations, we learn implicit representations of occupanc…
Dexterous Manipulation of Deformable Objects via Pneumatic Gripping: Lifting by One End
Osaka University（大阪大学）；National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究所（AIST））；The University of Texas at Austin（得克萨斯大学奥斯汀分校）；Ternopil Ivan Puluj National Technical University（捷尔诺波尔伊万·普卢伊国立技术大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）
Collocated tactile sensing is a fundamental enabling technology for dexterous manipulation. However, deformable sensors introduce complex dynamics between the robot, grasped object, and environment that must be considered for fine manipulation. Here, we propose a method to learn soft tactile sensor membrane dynamics that accounts for sensor deformations caused by the physical interaction between the grasped object and environment. Our method combines the perceived 3D geometry of the membrane with proprioceptive reaction wrenches to predict future deformations conditioned on robot action. Grasp…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；City University of Hong Kong（香港城市大学）
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 …
Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）
The deformable linear objects (DLOs) are common in both industrial and domestic applications, such as wires, cables, ropes. Because of its highly deformable nature, it is difficult for the robot to reproduce human's dexterous skills on DLOs. In this paper, the unknown deformation model is estimated in both the offline and online manners. The offline learning aims to provide a good approximation prior to the manipulation task, while the online learning aims to compensate the errors due to insufficient training (e.g. limited datasets) in the offline phase. The offline module works by constructin…
Robotic deformable-object manipulation is a challenge in the robotic industry because deformable objects have complicated and various object states. Predicting those object states and updating manipulation planning is time-consuming and computationally expensive. In this paper, we propose learning known configurations of garments to allow a robot to recognise garment states and choose a pre-designed manipulation plan for garment flattening.
Sashimi-Bot: Autonomous Tri-manual Advanced Manipulation and Cutting of Deformable Objects
Robotics and Intelligent Automation（机器人与智能自动化）；SINTEF Ocean（SINTEF海洋）；Department of Computer Science（计算机科学系）；NTNU - Norwegian University of Science and Technology（挪威科学与技术大学）；QUT Centre for Robotics（昆士兰大学机器人中心）；Queensland University of Technology（昆士兰技术大学）；Inria, University of Rennes, CNRS, IRISA（Inria、里昂大学、法国国家科学研究中心、IRISA）；CSAIL（计算机科学与人工智能实验室）；MIT - Massachusets Institute of Technology（麻省理工学院）；RealTek；Norwegian University of Life Sciences (NMBU)（挪威生命科学大学（NMBU））
Shape-Space Deformer: Unified Visuo-Tactile Representations for Robotic Manipulation of Deformable Objects
CSIRO Robotics, Data61, CSIRO（CSIRO机器人部、Data61、CSIRO）；School of Electrical Engineering and Computer Science (EECS), The University of Queensland（电气工程与计算机科学学院（EECS）、昆士兰大学）
Robot manipulation of rope-like objects is an interesting problem that has some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a model-based method that could be used to solve a large variety of optimization problems. Towards this end, we introduced compliant, position…
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息学研究所）；Worcester Polytechnic Institute（伍斯特理工学院）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
Benchmarking of robotic manipulations is one of the open issues in robotic research. An important factor that has enabled progress in this area in the last decade is the existence of common object sets that have been shared among different research groups. However, the existing object sets are very limited when it comes to cloth-like objects that have unique particularities and challenges. This paper is a first step towards the design of a cloth object set to be distributed among research groups from the robotics cloth manipulation community. We present a set of household cloth objects and rel…
Harbin Institute of Technology（哈尔滨工业大学）；Beijing Institute of Control Engineering（北京控制工程研究所）；TU Delft（代尔夫特理工大学）；The Hong Kong Polytechnic University（香港理工大学）
The robotic manipulation of composite rigid-deformable objects (i.e. those with mixed non-homogeneous stiffness properties) is a challenging problem with clear practical applications that, despite the recent progress in the field, it has not been sufficiently studied in the literature. To deal with this issue, in this paper we propose a new visual servoing method that has the capability to manipulate this broad class of objects (which varies from soft to rigid) with the same adaptive strategy. To quantify the object's infinite-dimensional configuration, our new approach computes a compact feed…
University of Bologna（博洛尼亚大学）；Menoufia University（曼努菲亚大学）
Deformable Linear Objects (DLOs) such as ropes, cables, and surgical sutures have a wide variety of uses in automotive engineering, surgery, and electromechanical industries. Therefore, modeling of DLOs as well as a computationally efficient way to predict the DLO behavior are of great importance, in particular to enable robotic manipulation of DLOs. The main motivation of this work is to enable efficient prediction of the DLO behavior during robotic manipulation. In this paper, the DLO is modeled by a multivariate dynamic spline, while a symplectic integration method is used to solve the mode…
KTH, Royal Institute of Technology（瑞典皇家理工学院）；CAS/RPL；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人形与机器人研究所）
Capturing scene dynamics and predicting the future scene state is challenging but essential for robotic manipulation tasks, especially when the scene contains both rigid and deformable objects. In this work, we contribute a simulation environment and generate a novel dataset for task-specific manipulation, involving interactions between rigid objects and a deformable bag. The dataset incorporates a rich variety of scenarios including different object sizes, object numbers and manipulation actions. We approach dynamics learning by proposing an object-centric graph representation and two modules…
Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need for large amounts of data that can only be satisfied in simulation. This paper proposes a new shape control task for Deformable Linear Objects (DLOs). More notably, we present the first study on the eff…
We demonstrate model-based, visual robot manipulation of linear deformable objects. Our approach is based on a state-space representation of the physical system that the robot aims to control. This choice has multiple advantages, including the ease of incorporating physics priors in the dynamics model and perception model, and the ease of planning manipulation actions. In addition, physical states can naturally represent object instances of different appearances. Therefore, dynamics in the state space can be learned in one setting and directly used in other visually different settings. This is…
Tencent Robotics X Lab（腾讯机器人X实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects. We present a novel framework, Graph-Transporter, for goal-conditioned deformable object rearranging tasks. To tackle the challenge of complex configuration space and dynamics, we represent the configuration space of a deformable object with a graph structure and the graph features are encoded by a graph convolution network. Our framework adopts an architecture based on Fully Convolutional Network (FCN) to output p…
Aalto University（阿尔托大学）；Czech Technical University in Prague（布拉格捷克理工大学）
Planning accurate manipulation for deformable objects requires prediction of their state. The prediction is often complicated by a loss of stability that may result in collapse of the deformable object. In this work, stability of a fabric strip folding performed by a robot is studied. We show that there is a static instability in the folding process. This instability is detected in a physics-based simulation and the position of the instability is verified experimentally by real robotic manipulation. Three state-of-the-art methods for folding are assessed in the presence of static instability. …
TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Region Grounding
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
Fudan University, China（复旦大学）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Shanghai Jiao Tong University, China（上海交通大学）；The Chinese University of Hong Kong, China（香港中文大学）；The University of Hong Kong, China（香港大学）
Tsinghua University（清华大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）
Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, shape control of DLOs is challenging, especially for large deformation control which requires global and more accurate models. In this paper, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offli…
The physical interaction of aerial robots with their environment has countless potential applications and is an emerging area with many open challenges. Fully-actuated multirotors have been introduced to tackle some of these challenges. They provide complete control over position and orientation and eliminate the need for attaching a multi-DoF manipulation arm to the robot. However, there are many open problems before they can be used in real-world applications. Researchers have introduced some methods for physical interaction in limited settings. Their experiments primarily use prototype-leve…
We present a framework for the simulation of rigid and deformable bodies in the presence of contact and friction. Our method is based on a non-smooth Newton iteration that solves the underlying nonlinear complementarity problems (NCPs) directly. This approach allows us to support nonlinear dynamics models, including hyperelastic deformable bodies and articulated rigid mechanisms, coupled through a smooth isotropic friction model. The fixed-point nature of our method means it requires only the solution of a symmetric linear system as a building block. We propose a new complementarity preconditi…
