Contour Moments Based Manipulation of Composite Rigid-Deformable Objects with Finite Time Model Estimation and Shape/Position Control
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 feedback vector of 2D contour moments features. A sliding mode control scheme is then designed to simultaneously ensure the finite-time convergence of both the feedback shape error and the model estimation error. The stability of the proposed framework (including the boundedness of all the signals) is rigorously proved with Lyapunov theory. Detailed simulations and experiments are presented to validate the effectiveness of the proposed approach. To the best of the author's knowledge, this is the first time that contour moments along with finite-time control have been used to solve this difficult manipulation problem.
Aalto University（阿尔托大学）；Technische Universität München（慕尼黑工业大学）
Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactions. The contact prediction model is trained on synthetic data generated by a developed simulation environment to learn the mapping from point-cloud observation of a rigid target object and the pose of a deformable tool, to 3D representation…
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…
Grasping deformable objects is not well researched due to the complexity in modelling and simulating the dynamic behavior of such objects. However, with the rapid development of physics-based simulators that support soft bodies, the research gap between rigid and deformable objects is getting smaller. To leverage the capability of such simulators and to challenge the assumption that has guided robotic grasping research so far, i.e., object rigidity, we proposed a deep-learning based approach that generates stiffness-dependent grasps. Our network is trained on purely synthetic data generated fr…
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…
LIRMM - Université de Montpellier CNRS（法国国家科学研究中心蒙彼利埃大学信息、机器人、微电子学实验室）；Delft University of Technology（代尔夫特理工大学）；Honda Research Institute, Europe（本田欧洲研究所）；The Hong Kong Polytechnic University（香港理工大学）
This paper proposes a unified vision-based manipulation framework using image contours of deformable/rigid objects. Instead of using human-defined cues, the robot automatically learns the features from processed vision data. Our method simultaneously generates -- from the same data -- both, visual features and the interaction matrix that relates them to the robot control inputs. Extraction of the feature vector and control commands is done online and adaptively, with little data for initialization. The method allows the robot to manipulate an object without knowing whether it is rigid or defor…
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 …
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…
University of California, Berkeley（加州大学伯克利分校）；NVIDIA Corporation（英伟达公司）；University of Utah（犹他大学）；University of Washington（华盛顿大学）
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery, and household automation. However, developing grasp strategies for such objects is uniquely challenging. Unlike rigid objects, deformable objects have infinite degrees of freedom and require field quantities (e.g., deformation, stress) to fully define their state. As these quantities are not easily accessible in the real world, we propose studying interaction with deformable objects through physics-based simulation…
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 e…
Coordinated Manipulation of Hybrid Deformable-Rigid Objects in Constrained Environments
Department of Mechanical and Nuclear Engineering, Khalifa University（卡利法大学机械与核工程系）；Khalifa University Center for Autonomous Robotic Systems (KUCARS)（卡利法大学自主机器人系统中心）；Department of Computer Science, Khalifa University（卡利法大学计算机科学系）
We propose an algorithm that uses energy mini- mization to estimate the current configuration of a non-rigid object. Our approach utilizes an RGBD image to calculate corresponding SURF features, depth, and boundary informa- tion. We do not use predetermined features, thus enabling our system to operate on unmodified objects. Our approach relies on a 3D nonlinear energy minimization framework to solve for the configuration using a semi-implicit scheme. Results show various scenarios of dynamic posters and shirts in different configurations to illustrate the performance of the method. In particu…
KTH Royal Institute of Technology（瑞典皇家理工学院）；University of Salerno（萨莱诺大学）；University of Cassino and Southern Lazio（卡西诺大学和南拉齐奥大学）
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects. Planning is performed in a low-dimensional latent state space that embeds images. We define and implement a Latent Space Roadmap (LSR) which is a graph-based structure that globally captures the latent system dynamics. Our framework consists of two main components: a Visual Foresight Module (VFM) that generates a visual plan as a sequence of images, and an Action Proposal Network (APN) that predicts the actions between them. We show the …
Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first…
Hybrid Gripper with Passive Pneumatic Soft Joints for Grasping Deformable Thin Objects
School of Mechanical Engineering, Hanoi University of Science and Technology（河内科技大学机械工程学院）；Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）
DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Zhongguancun Academy（中关村学院）；Microsoft Research（微软研究院）
We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. M…
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…
DeformGS: Scene Flow in Highly Deformable Scenes for Deformable Object Manipulation
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；National University of Singapore（新加坡国立大学）；Technical University of Munich（慕尼黑工业大学）；NVIDIA（英伟达）
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；NVIDIA（英伟达）；University of Washington（华盛顿大学）；The University of Sydney（悉尼大学）
Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise definition of the state space and its dynamics. While this is appropriate for rigid objects and robot states, it is challenging to define the state space of a deformable object and how it evolves in time. In this work, we pose the problem of inferring physical parameters of deformable objects as a probabilistic inference task defined with a simulator. We propose a novel methodology for extracting state information from …
Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor
School of Mechanical Engineering, Anhui University of Technology（安徽工业大学机械工程学院）；School of Engineering and Technology, China University of Geosciences (Beijing)（中国地质大学（北京）工程技术学院）
Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods. However, existing reinforcement learning benchmarks only cover tasks with direct state observability and simple low-dimensional dynamics or with relatively simple image-based environments, such as those with rigid objects. In this paper, we present SoftGym, a set of open-source simulated benchmarks for manipu…
ARC Centre of Excellence for Robotic Vision, Queensland University of Technology（昆士兰科技大学澳大利亚研究委员会机器人视觉卓越中心）；ARC Centre of Excellence for Robotic Vision, Monash University（莫纳什大学澳大利亚研究委员会机器人视觉卓越中心）
We address the problem of shaping deformable plastic materials using non-prehensile actions. Shaping plastic objects is challenging, since they are difficult to model and to track visually. We study this problem, by using kinetic sand, a plastic toy material which mimics the physical properties of wet sand. Inspired by a pilot study where humans shape kinetic sand, we define two types of actions: \textit{pushing} the material from the sides and \textit{tapping} from above. The chosen actions are executed with a robotic arm using image-based visual servoing. From the current and desired view of…
With the field of rigid-body robotics having matured in the last fifty years, routing, planning, and manipulation of deformable objects have recently emerged as a more untouched research area in many fields ranging from surgical robotics to industrial assembly and construction. Routing approaches for deformable objects which rely on learned implicit spatial representations (e.g., Learning-from-Demonstration methods) make them vulnerable to changes in the environment and the specific setup. On the other hand, algorithms that entirely separate the spatial representation of the deformable object …
