Current anthropomorphic robotic hands mainly focus on improving their dexterity by devising new mechanical structures and actuation systems. However, most of them rely on a single structure/system (e.g., bone-only) and ignore the fact that the human hand is composed of multiple functional structures (e.g., skin, bones, muscles, and tendons). This not only increases the difficulty of the design process but also lowers the robustness and flexibility of the fabricated hand. Besides, other factors like customization, the time and cost for production, and the degree of resemblance between human hands and robotic hands, remain omitted. To tackle these problems, this study proposes a 3D printable multi-layer design that models the hand with the layers of skin, tissues, and bones. The proposed design first obtains the 3D surface model of a target hand via 3D scanning, and then generates the 3D bone models from the surface model based on a fast template matching method. To overcome the disadvantage of the rigid bone layer in deformation, the tissue layer is introduced and represented by a concentric tube based structure, of which the deformability can be explicitly controlled by a parameter. Besides, a low-cost yet effective underactuated system is adopted to drive the fabricated hand. The proposed design is tested with 33 widely used object grasping types, as well as special objects like fragile silken tofu, and outperforms previous designs remarkably. With the proposed design, anthropomorphic robotic hands can be produced fast with low cost, and be customizable and deformable.
City University of Hong Kong（香港城市大学）；the University of Hong Kong（香港大学）
To achieve human-like dexterity for anthropomorphic robotic hands, it is essential to understand the biomechanics and control strategies of the human hand, in order to reduce the number of actuators being used without loosing hand flexibility. To this end, in this article, we propose a new interpretation about the working mechanism of the metacarpal (MCP) joint's extension and the underlying control strategies of the human hand, based on which we further propose a highly flexible finger design to achieve independent movements of interphalangeal (IP) joints and MCP joint. Besides, we consider t…
University of Bristol（布里斯托大学）；Universit di Pisa（比萨大学）；Istituto Italiano di Tecnologia（意大利理工学院）
In this work, we report on the integrated sensorimotor control of the Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a soft biomimetic optical tactile sensor based on the human sense of touch. Our focus is how a sense of touch can be used to control an anthropomorphic hand with one degree of actuation, based on an integration that respects the hand's mechanical functionality. We consider: (i) closed-loop tactile control to establish a light contact on an unknown held object, based on the struct…
Most current anthropomorphic robotic hands can realize part of the human hand functions, particularly for object grasping. However, due to the complexity of the human hand, few current designs target at daily object manipulations, even for simple actions like rotating a pen. To tackle this problem, we introduce a gesture based framework, which adopts the widely-used 33 grasping gestures of Feix as the bases for hand design and implementation of manipulation. In the proposed framework, we first measure the motion ranges of human fingers for each gesture, and based on the results, we propose a s…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Teleoperation (i.e., controlling a robot with human motion) proves promising in enabling a humanoid robot to move as dynamically as a human. But how to map human motion to a humanoid robot matters because a human and a humanoid robot rarely have identical topologies and dimensions. This work presents an experimental study that utilizes reaction tests to compare the proposed joint space mapping and the proposed task space mapping for dynamic teleoperation of an anthropomorphic robotic arm that possesses human-level dynamic motion capabilities. The experimental results suggest that the robot ach…
Do Robots Really Need Anthropomorphic Hands? A Comparison of Human and Robotic Hands
Robotics Innovation Center, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)（德意志人工智能研究中心机器人创新中心）；Leibniz Universität Hannover, L3S Research Center（汉诺威莱布尼茨大学L3S研究中心）
Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver
Centre for Applied Autonomous Sensor Systems (AASS)（应用自主传感器系统中心（AASS））；Örebro University（厄勒布鲁大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；University of Skövde（舍夫德大学）；TU Munich（慕尼黑工业大学）
Institut de Recherche en Communications et Cybernétique de Nantes（南特通信与控制论研究所）；Aldebaran（阿尔德巴兰）；LUNAM University（卢南大学）；University of Poitiers（普瓦捷大学）
This paper presents a numerical method to conceive and design the kinematic model of an anthropomorphic robotic hand used for gesturing and grasping. In literature, there are few numerical methods for the finger placement of human-inspired robotic hands. In particular, there are no numerical methods, for the thumb placement, that aim to improve the hand dexterity and grasping capabilities by keeping the hand design close to the human one. While existing models are usually the result of successive parameter adjustments, the proposed method determines the fingers placements by mean of empirical …
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Technical University Darmstadt（达姆施塔特工业大学）
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions between expert and learner, when the embodiments of the agents are different (morphology, dynamics, degrees of freedom, etc.). Many existing approaches in imitation learning circumvent the correspondence problem, for example, kinesthetic teaching or teleoperation, which are performed…
Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force output, small actuation range, and poor compactness. Despite advances in this area, realizing compact soft grippers with high dexterity and force output is still challenging. This paper explores twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by …
Identification and validation of the dynamic model of a tendon-driven anthropomorphic finger
Munich Institute of Robotics & Machine Intelligence, Technische Universität München (TUM)（慕尼黑机器人与机器智能研究所，慕尼黑工业大学（TUM））；Centre for Tactile Internet with Human-in-the-Loop (CeTI)（以人为中心的触觉互联网中心（CeTI））
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 Stuttgart（斯图加特大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Hertie-Institute for Clinical Brain Research, University of Tübingen（赫蒂临床脑研究所，图宾根大学）
Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles when learning from scratch. Our study closes this gap and showcases the potential of muscle actuators for …
DigiArm: An Anthropomorphic 3D-Printed Prosthetic Hand with Enhanced Dexterity for Typing Tasks
Department of Computer Science, Technion, Haifa, Israel（计算机科学系，技术学院，海法，以色列）；Department of Mechanical Engineering, Technion, Haifa, Israel（机械工程系，技术学院，海法，以色列）
Teleoperation is a valuable tool for robotic manipulators in highly unstructured environments. However, finding an intuitive mapping between a human hand and a non-anthropomorphic robot hand can be difficult, due to the hands' dissimilar kinematics. In this paper, we seek to create a mapping between the human hand and a fully actuated, non-anthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users. To accomplish this, we propose a low-dimensional teleoperation subspace which can be used as an intermediary for mapping between hand pose…
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany（慕尼黑机器人与机器智能研究所（MIRMI），慕尼黑技术大学（TUM））；Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学）
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…
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…
Human-Inspired Soft Anthropomorphic Hand System for Neuromorphic Object and Pose Recognition Using Multimodal Signals
Institute for Cognitive Systems, Technical University of Munich（认知系统研究所，慕尼黑技术大学）；Department of Biomedical Engineering, Johns Hopkins University（生物医学工程系，约翰霍普金斯大学）
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）
University of California, Berkeley（加州大学伯克利分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
This paper presents a novel trajectory optimization formulation to solve the robotic assembly of the belt drive unit. Robotic manipulations involving contacts and deformable objects are challenging in both dynamic modeling and trajectory planning. For modeling, variations in the belt tension and contact forces between the belt and the pulley could dramatically change the system dynamics. For trajectory planning, it is computationally expensive to plan trajectories for such hybrid dynamical systems as it usually requires planning for discrete modes separately. In this work, we formulate the bel…
Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation
Department of Biological and Agricultural Engineering, University of Arkansas（阿肯色大学，生物与农业工程系）；Department of Food Science, University of Arkansas（阿肯色大学，食品科学系）
Using a deformable sheet to handle objects is convenient and found in many practical applications. For object manipulation through a deformable sheet that is held by multiple mobile robots, it is a challenging task to model the object-sheet interactions. We present a computational model and algorithm to capture the object position on the deformable sheet with changing robotic team formations. A virtual variable cables model (VVCM) is proposed to simplify the modeling of the robot-sheet-object system. With the VVCM, we further present a motion planner for the robotic team to transport the objec…
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. …
Robotic manipulation of deformable objects remains a challenging task. One such task is folding a garment autonomously. Given start and end folding positions, what is an optimal trajectory to move the robotic arm to fold a garment? Certain trajectories will cause the garment to move, creating wrinkles, and gaps, other trajectories will fail altogether. We present a novel solution to find an optimal trajectory that avoids such problematic scenarios. The trajectory is optimized by minimizing a quadratic objective function in an off-line simulator, which includes material properties of the garmen…
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）
Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters
Czech Technical University in Prague（布拉格捷克理工大学）；Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克理工大学电气工程学院）；Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague（布拉格捷克理工大学捷克信息学、机器人学与控制论研究所）
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 …
Control strategies for robotic needle steering in soft tissues must account for complex interactions between the needle and the tissue to achieve accurate needle tip positioning. Recent findings show faster robotic command rate can improve the control stability in realistic scenarios. This study proposes the use of Extreme Learning Machines to provide fast commands for robotic needle steering. A synthetic dataset based on the inverse finite element simulation control framework is used to train the model. Results show the model is capable to infer commands 66% faster than the inverse simulation…
Tactile SoftHand-A: 3D-Printed, Tactile, Highly-underactuated, Anthropomorphic Robot Hand with an Antagonistic Tendon Mechanism
School of Engineering Mathematics and Technology（工程数学与技术学院）；Bristol Robotics Laboratory（布里斯托尔机器人实验室）；University of Bristol（布里斯托尔大学）；School of Robotics（机器人学院）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Department of Information Engineering（信息工程系）；Research Center "E.Piaggio"（E.Piaggio研究中心）；University of Pisa（比萨大学）；Soft Robotics for Human Cooperation and Rehabilitation（人机合作与康复软机器人）；Istituto Italiano di Tecnologia（意大利技术研究院）
