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Journal articles on the topic 'Hand manipulation'

Author: Grafiati

Published: 9 March 2023

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1

Bircher, Walter G., Andrew S. Morgan, and Aaron M. Dollar. "Complex manipulation with a simple robotic hand through contact breaking and caging." Science Robotics 6, no. 54 (May 12, 2021): eabd2666. http://dx.doi.org/10.1126/scirobotics.abd2666.

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Humans use all surfaces of the hand for contact-rich manipulation. Robot hands, in contrast, typically use only the fingertips, which can limit dexterity. In this work, we leveraged a potential energy–based whole-hand manipulation model, which does not depend on contact wrench modeling like traditional approaches, to design a robotic manipulator. Inspired by robotic caging grasps and the high levels of dexterity observed in human manipulation, a metric was developed and used in conjunction with the manipulation model to design a two-fingered dexterous hand, the Model W. This was accomplished by simulating all planar finger topologies composed of open kinematic chains of up to three serial revolute and prismatic joints, forming symmetric two-fingered hands, and evaluating their performance according to the metric. We present the best design, an unconventional robot hand capable of performing continuous object reorientation, as well as repeatedly alternating between power and pinch grasps—two contact-rich skills that have often eluded robotic hands—and we experimentally characterize the hand’s manipulation capability. This hand realizes manipulation motions reminiscent of thumb–index finger manipulative movement in humans, and its topology provides the foundation for a general-purpose dexterous robot hand.

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Tanikawa, Tamio, Masashi Kawai, Noriho Koyachi, Tatsuo Arai, Takayuki Ide, Shinji Kaneko, Ryo Ohta, and Takeshi Hirose. "Force Control System for Autonomous Micro Manipulation." Journal of Robotics and Mechatronics 14, no. 3 (June 20, 2002): 212–20. http://dx.doi.org/10.20965/jrm.2002.p0212.

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A dexterous micro manipulation system was developed for applications such as assembling micro machines, manipulating cells, and micro surgery. We have proposed a concept of a two-fingered micro hand, designed and built a prototype. We succeeded in performing basic micro manipulations with a teleoperation, including the grasp, release, and rotation of a microscopic object. The micro hand is controlled with a position control only. An operator has to guess a micro grasping force on the object from a microscope image. The accurate micro manipulation depends on a skill of the operator yet. For an easy manipulation and an automatic manipulation, it is necessary to measure the micro forces between the finger and the object. A micro force sensor has developed for a force control in micro manipulation on a corroboration research of AIST and Olympus Optical Co., Ltd. Its resolution is 0.5 nN in theoretically. In this paper, we will mention the micro force sensor and to perform an automatic micro manipulation with installing the sensor and a force control system. Basic experiment shows excellent micro capability.

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Lopez, Patricio Rivera, Ji-Heon Oh, Jin Gyun Jeong, Hwanseok Jung, Jin Hyuk Lee, Ismael Espinoza Jaramillo, Channabasava Chola, Won Hee Lee, and Tae-Seong Kim. "Dexterous Object Manipulation with an Anthropomorphic Robot Hand via Natural Hand Pose Transformer and Deep Reinforcement Learning." Applied Sciences 13, no. 1 (December 28, 2022): 379. http://dx.doi.org/10.3390/app13010379.

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Dexterous object manipulation using anthropomorphic robot hands is of great interest for natural object manipulations across the areas of healthcare, smart homes, and smart factories. Deep reinforcement learning (DRL) is a particularly promising approach to solving dexterous manipulation tasks with five-fingered robot hands. Yet, controlling an anthropomorphic robot hand via DRL in order to obtain natural, human-like object manipulation with high dexterity remains a challenging task in the current robotic field. Previous studies have utilized some predefined human hand poses to control the robot hand’s movements for successful object-grasping. However, the hand poses derived from these grasping taxonomies are limited to a partial range of adaptability that could be performed by the robot hand. In this work, we propose a combinatory approach of a deep transformer network which produces a wider range of natural hand poses to configure the robot hand’s movements, and an adaptive DRL to control the movements of an anthropomorphic robot hand according to these natural hand poses. The transformer network learns and infers the natural robot hand poses according to the object affordance. Then, DRL trains a policy using the transformer output to grasp and relocate the object to the designated target location. Our proposed transformer-based DRL (T-DRL) has been tested using various objects, such as an apple, a banana, a light bulb, a camera, a hammer, and a bottle. Additionally, its performance is compared with a baseline DRL model via natural policy gradient (NPG). The results demonstrate that our T-DRL achieved an average manipulation success rate of 90.1% for object manipulation and outperformed NPG by 24.8%.

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Hazard, Christopher, Nancy Pollard, and Stelian Coros. "Automated Design of Robotic Hands for In-Hand Manipulation Tasks." International Journal of Humanoid Robotics 17, no. 01 (January 8, 2020): 1950029. http://dx.doi.org/10.1142/s0219843619500294.

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Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

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5

Odhner, Lael U., Raymond R. Ma, and Aaron M. Dollar. "Exploring Dexterous Manipulation Workspaces with the iHY Hand." Journal of the Robotics Society of Japan 32, no. 4 (2014): 318–22. http://dx.doi.org/10.7210/jrsj.32.318.

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6

Cruciani, Silvia, Balakumar Sundaralingam, Kaiyu Hang, Vikash Kumar, Tucker Hermans, and Danica Kragic. "Benchmarking In-Hand Manipulation." IEEE Robotics and Automation Letters 5, no. 2 (April 2020): 588–95. http://dx.doi.org/10.1109/lra.2020.2964160.

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7

Liu, Ming-Jin, Cai-Hua Xiong, and Di Hu. "Assessing the manipulative potentials of monkeys, apes and humans from hand proportions: implications for hand evolution." Proceedings of the Royal Society B: Biological Sciences 283, no. 1843 (November 30, 2016): 20161923. http://dx.doi.org/10.1098/rspb.2016.1923.

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The hand structure possesses a greater potential for performing manipulative skills than is typically observed, whether in humans or non-human anthropoids. However, a precise assessment of the potential manipulative skills of hands has been challenging, which hampers our understanding of the evolution of manipulative abilities in anthropoid hands. Here, we establish a functional model to quantitatively infer the manipulative potentials of anthropoid hands based on hand proportions. Our results reveal a large disparity of manipulative potentials among anthropoid hands. From the aspect of hand proportions, the human hand has the best manipulative potential among anthropoids. However, the species with a manipulative potential closer to that of humans are not our nearest relatives, chimpanzees, but rather, are certain monkey species. In combination with the phylogenetically informed morphometric analyses, our results suggest that the morphological changes of non-human anthropoid hands did not coevolve with the brain to facilitate the manipulative ability during the evolutionary process, although the manipulative ability is a survival skill. The changes in non-human anthropoid hands may have more likely evolved under selective pressure for locomotion than manipulation.

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8

Hang, Kaiyu, Walter G. Bircher, Andrew S. Morgan, and Aaron M. Dollar. "Hand–object configuration estimation using particle filters for dexterous in-hand manipulation." International Journal of Robotics Research 39, no. 14 (October 17, 2019): 1760–74. http://dx.doi.org/10.1177/0278364919883343.

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We consider the problem of in-hand dexterous manipulation with a focus on unknown or uncertain hand–object parameters, such as hand configuration, object pose within hand, and contact positions. In particular, in this work we formulate a generic framework for hand–object configuration estimation using underactuated hands as an example. Owing to the passive reconfigurability and the lack of encoders in the hand’s joints, it is challenging to estimate, plan, and actively control underactuated manipulation. By modeling the grasp constraints, we present a particle filter-based framework to estimate the hand configuration. Specifically, given an arbitrary grasp, we start by sampling a set of hand configuration hypotheses and then randomly manipulate the object within the hand. While observing the object’s movements as evidence using an external camera, which is not necessarily calibrated with the hand frame, our estimator calculates the likelihood of each hypothesis to iteratively estimate the hand configuration. Once converged, the estimator is used to track the hand configuration in real time for future manipulations. Thereafter, we develop an algorithm to precisely plan and control the underactuated manipulation to move the grasped object to desired poses. In contrast to most other dexterous manipulation approaches, our framework does not require any tactile sensing or joint encoders, and can directly operate on any novel objects, without requiring a model of the object a priori. We implemented our framework on both the Yale Model O hand and the Yale T42 hand. The results show that the estimation is accurate for different objects, and that the framework can be easily adapted across different underactuated hand models. In the end, we evaluated our planning and control algorithm with handwriting tasks, and demonstrated the effectiveness of the proposed framework.

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9

Ito, Koji. "Hand Manipulation and Impedance Adjustment." Journal of Robotics and Mechatronics 7, no. 2 (April 20, 1995): 161–68. http://dx.doi.org/10.20965/jrm.1995.p0161.

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10

Andrychowicz, OpenAI: Marcin, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, et al. "Learning dexterous in-hand manipulation." International Journal of Robotics Research 39, no. 1 (November 18, 2019): 3–20. http://dx.doi.org/10.1177/0278364919887447.

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We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM .

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11

Shi, Jian, J. Zachary Woodruff, Paul B. Umbanhowar, and Kevin M. Lynch. "Dynamic In-Hand Sliding Manipulation." IEEE Transactions on Robotics 33, no. 4 (August 2017): 778–95. http://dx.doi.org/10.1109/tro.2017.2693391.

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12

Lu, Qiujie, Nicholas Baron, Angus B. Clark, and Nicolas Rojas. "Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper." International Journal of Robotics Research 40, no. 12-14 (October 22, 2021): 1402–18. http://dx.doi.org/10.1177/02783649211048929.

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We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilizes a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers, jointly controlled by a single actuator, connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.

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13

Bütepage, Judith, Silvia Cruciani, Mia Kokic, Michael Welle, and Danica Kragic. "From Visual Understanding to Complex Object Manipulation." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 161–79. http://dx.doi.org/10.1146/annurev-control-053018-023735.

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Planning and executing object manipulation requires integrating multiple sensory and motor channels while acting under uncertainty and complying with task constraints. As the modern environment is tuned for human hands, designing robotic systems with similar manipulative capabilities is crucial. Research on robotic object manipulation is divided into smaller communities interested in, e.g., motion planning, grasp planning, sensorimotor learning, and tool use. However, few attempts have been made to combine these areas into holistic systems. In this review, we aim to unify the underlying mechanics of grasping and in-hand manipulation by focusing on the temporal aspects of manipulation, including visual perception, grasp planning and execution, and goal-directed manipulation. Inspired by human manipulation, we envision that an emphasis on the temporal integration of these processes opens the way for human-like object use by robots.

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14

Chaigneau, D., M. Arsicault, J. P. Gazeau, and S. Zeghloul. "LMS robotic hand grasp and manipulation planning (an isomorphic exoskeleton approach)." Robotica 26, no. 2 (March 2008): 177–88. http://dx.doi.org/10.1017/s0263574707003736.

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SUMMARYIn order to widen the potentialities of manipulation of the Laboratoire de Mécanique des solides (LMS) mechanical hand, we developed a new planning approach based on the use of a specific exoskeleton. This one has kinematics architecture and dimensions identical to the mechanical hand. This feature allows us to obtain manipulation trajectories for the mechanical hand, very easily and very quickly, by using the exoskeleton, without complex calibration. Manipulation's trajectories are replayed offline with an autonomous control, and, consequently, the exoskeleton is not used with any feedback strategy for telemanipulation. This paper presents the characteristics of this exoskeleton and the graphic interface that we developed. This one uses a method to determine the object's evolution during the manipulation with the exoskeleton, without using exteroceptive sensors. This new approach was tested for standard trajectories by simulation on a Computer-aided design (CAD) robotics system and by using the mechanical hand. Thus, we validate the use concept of an isomorphic exoskeleton to mechanical hand for manipulation planning with the LMS mechanical hand.

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15

Mcsp, Isabel Cary Bsc, and Jo Adams Dipcot. "A Comparison of Dominant and Non-dominant Hand Function in both Right- and Left-Handed Individuals using the Southampton Hand Assessment Procedure (SHAP)." British Journal of Hand Therapy 8, no. 1 (March 2003): 4–10. http://dx.doi.org/10.1177/175899830300800101.

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There have been several studies investigating the difference in hand strength between the dominant and non-dominant hands (Petersen et al 1989, Crosby et al 1994, Armstrong and Oldham 1999, Amousun et al 1995). However, there is a lack of literature investigating hand function, which may be a more useful measure of daily hand use. Differences in dominant and non-dominant hand function are important, as clinically, bilateral functional comparison is recommended to the therapist (Boscheinen-Morrin et al 1992). This preliminary research study aimed to: • investigate the difference in grip manipulation between the dominant and non-dominant hands in a sample of 24 healthy participants using the Southampton Hand Assessment Procedure (SHAP) • explore the difference in use of the hands between right- and left-hand dominant individuals by means of a short questionnaire. The results demonstrated that right-handers preferred to use their dominant hand for all skilled activities whereas left-handers showed some preference for their non-dominant hand. Only the light tripod grip task for right-handers showed a statistically significant difference (p=0.009) between the speed of manipulation between dominant and non-dominant hands; all other light and heavy handgrips demonstrated no statistical difference. However, there was a general trend for the dominant hand to be faster in manipulating objects than the non-dominant hand in both right- and left-handed individuals.

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FUNABASHI, Satoshi, Takashi SATO, Alexander SCHMITZ, and Shigeki SUGANO. "Feature Extraction by Deep Learning for Improved In-Hand Manipulation." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 31–32. http://dx.doi.org/10.1299/jsmeicam.2015.6.31.

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17

Arif, Tariq Mohammad, and Teruaki Ito. "211 Implementation of hand gesture interface for robotic arm manipulation." Proceedings of Conference of Chugoku-Shikoku Branch 2011.49 (2011): 57–58. http://dx.doi.org/10.1299/jsmecs.2011.49.57.

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18

Hang, Kaiyu, Walter G. Bircher, Andrew S. Morgan, and Aaron M. Dollar. "Manipulation for self-Identification, and self-Identification for better manipulation." Science Robotics 6, no. 54 (May 19, 2021): eabe1321. http://dx.doi.org/10.1126/scirobotics.abe1321.

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The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand’s actuation input to the object’s motion to be obtained. Without assuming that most of these model parameters are known a priori or can be easily estimated by sensors, we focus on equipping robots with the ability to actively self-identify necessary model parameters using minimal sensing. Here, we derive algorithms, on the basis of the concept of virtual linkage-based representations (VLRs), to self-identify the underlying mechanics of hand-object systems via exploratory manipulation actions and probabilistic reasoning and, in turn, show that the self-identified VLR can enable the control of precise in-hand manipulation. To validate our framework, we instantiated the proposed system on a Yale Model O hand without joint encoders or tactile sensors. The passive adaptability of the underactuated hand greatly facilitates the self-identification process, because they naturally secure stable hand-object interactions during random exploration. Relying solely on an in-hand camera, our system can effectively self-identify the VLRs, even when some fingers are replaced with novel designs. In addition, we show in-hand manipulation applications of handwriting, marble maze playing, and cup stacking to demonstrate the effectiveness of the VLR in precise in-hand manipulation control.

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Kawasaki, Haruhisa, and Tsuneo Komatsu. "Mechanism Design of Anthropomorphic Robot Hand: Gifu Hand I." Journal of Robotics and Mechatronics 11, no. 4 (August 20, 1999): 269–73. http://dx.doi.org/10.20965/jrm.1999.p0269.

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This paper presents an anthropomorphic robot hand Gifu Hand I, to be used as a platform of robot hand for the study of dexterous manipulation. To perform grasping and manipulation like a human, the Gifu Hand I includes five fingers whose actuators are servomotors built in the palm and fingers. A thumb has four degreeof-freedom (DOF) and four joints, and fingers have three DOF and four joints. Two axes of joints near the palm cross orthogonally at one point like the human hand. The design concept of the anthropomorphic robot hand is presented and mechanisms and specifications of the developed robot hand are shown.

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Turlapati, Sri Harsha, and Domenico Campolo. "Towards Haptic-Based Dual-Arm Manipulation." Sensors 23, no. 1 (December 29, 2022): 376. http://dx.doi.org/10.3390/s23010376.

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Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand−object pose tracking faces challenges such as occlusions and objects moving out of view during robotic manipulation. In this work, we show that object kinematics can be inferred from local haptic feedback at the robot−object contact points, combined with robot kinematics information given an initial vision estimate of the object pose. A planar, dual-arm, teleoperated robotic setup was built to manipulate an object with hands shaped like circular discs. The robot hands were built with rubber cladding to allow for rolling contact without slipping. During stable grasping by the dual arm robot, under quasi-static conditions, the surface of the robot hand and object at the contact interface is defined by local geometric constraints. This allows one to define a relation between object orientation and robot hand orientation. With rolling contact, the displacement of the contact point on the object surface and the hand surface must be equal and opposite. This information, coupled with robot kinematics, allows one to compute the displacement of the object from its initial location. The mathematical formulation of the geometric constraints between robot hand and object is detailed. This is followed by the methodology in acquiring data from experiments to compute object kinematics. The sensors used in the experiments, along with calibration procedures, are presented before computing the object kinematics from recorded haptic feedback. Results comparing object kinematics obtained purely from vision and from haptics are presented to validate our method, along with the future ideas for perception via haptic manipulation.

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21

Togo, Shoichiro, and Hiroyuki Ukida. "UAV manipulation by hand gesture recognition." SICE Journal of Control, Measurement, and System Integration 15, no. 2 (June 16, 2022): 145–61. http://dx.doi.org/10.1080/18824889.2022.2103631.

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22

Johansson, Roland S., Göran Westling, Anders Bäckström, and J. Randall Flanagan. "Eye–Hand Coordination in Object Manipulation." Journal of Neuroscience 21, no. 17 (September 1, 2001): 6917–32. http://dx.doi.org/10.1523/jneurosci.21-17-06917.2001.

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23

Bauer, Dominik, Cornelia Bauer, Jonathan P. King, Daniele Moro, Kai-Hung Chang, Stelian Coros, and Nancy Pollard. "Design and Control of Foam Hands for Dexterous Manipulation." International Journal of Humanoid Robotics 17, no. 01 (January 6, 2020): 1950033. http://dx.doi.org/10.1142/s0219843619500336.

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There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This paper introduces foam robot hands actuated by tendons sewn through a fabric glove. The flexibility of tendon actuation allows for high competence in utilizing deformation for robust in-hand manipulation. We discuss manufacturing, control, and design optimization for foam robots and demonstrate robust grasping and in-hand manipulation on a variety of different physical hand prototypes.

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24

Feix, Thomas, Tracy L. Kivell, Emmanuelle Pouydebat, and Aaron M. Dollar. "Estimating thumb–index finger precision grip and manipulation potential in extant and fossil primates." Journal of The Royal Society Interface 12, no. 106 (May 2015): 20150176. http://dx.doi.org/10.1098/rsif.2015.0176.

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Primates, and particularly humans, are characterized by superior manual dexterity compared with other mammals. However, drawing the biomechanical link between hand morphology/behaviour and functional capabilities in non-human primates and fossil taxa has been challenging. We present a kinematic model of thumb–index precision grip and manipulative movement based on bony hand morphology in a broad sample of extant primates and fossil hominins. The model reveals that both joint mobility and digit proportions (scaled to hand size) are critical for determining precision grip and manipulation potential, but that having either a long thumb or great joint mobility alone does not necessarily yield high precision manipulation. The results suggest even the oldest available fossil hominins may have shared comparable precision grip manipulation with modern humans. In particular, the predicted human-like precision manipulation of Australopithecus afarensis , approximately one million years before the first stone tools, supports controversial archaeological evidence of tool-use in this taxon.

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Matsuno, Takayuki, Toshio Fukuda, Fumihito Arai, and Yasuhisa Hasegawa. "Flexible Rope Manipulation Using Elastic Deformation Modeling by Dual Manipulator System with Vision Sensor." Journal of Robotics and Mechatronics 16, no. 1 (February 20, 2004): 31–38. http://dx.doi.org/10.20965/jrm.2004.p0031.

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In this paper we propose a flexible object manipulation method by a dual manipulator system. A flexible object such a rope and paper is easily deformed and has hysteresis. Various approaches have been made on the research for the flexible object manipulation. However in the former research works, the manipulator system works only simple task. For more complex works with flexible object, the robot has to hand over the flexible object. So, we propose a flexible object recognition method which can hand over a flexible object using vision information and flexible object model. The dual manipulator system tied a cylinder object with flexible rope by repeating handing over actions in the experiment.

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26

Abondance, Sylvain, Clark B. Teeple, and Robert J. Wood. "A Dexterous Soft Robotic Hand for Delicate In-Hand Manipulation." IEEE Robotics and Automation Letters 5, no. 4 (October 2020): 5502–9. http://dx.doi.org/10.1109/lra.2020.3007411.

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27

Ueda, Jun, Masahiro Kondo, and Tsukasa Ogasawara. "The multifingered NAIST hand system for robot in-hand manipulation." Mechanism and Machine Theory 45, no. 2 (February 2010): 224–38. http://dx.doi.org/10.1016/j.mechmachtheory.2009.08.007.

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YAMAWAKI, Tasuku, Isamu KURIMOTO, and Masahito YASHIMA. "Trajectory Generation Method for In-Hand Manipulation with Robotic Hand." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2022 (2022): 2A2—N02. http://dx.doi.org/10.1299/jsmermd.2022.2a2-n02.

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29

Yao, Kunpeng, Dagmar Sternad, and Aude Billard. "Hand pose selection in a bimanual fine-manipulation task." Journal of Neurophysiology 126, no. 1 (July 1, 2021): 195–212. http://dx.doi.org/10.1152/jn.00635.2020.

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We study hand poses selection in bimanual fine motor skills. To understand how roles and control variables are distributed across the hands and fingers, we compared two conditions when unscrewing a screw from a watch face. When the watch face needed positioning, role distribution was strongly influenced by hand dominance; when the watch face was stationary, a variety of hand pose combinations emerged. Control of independent task demands is distributed either across hands or across distinct groups of fingers.

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Wang, Jin, Haoyuan Li, Fansheng Meng, Rui Ma, Quanbin Lai, Jianwu Li, and Xingguang Duan. "Control Strategy of Master-Slave Manipulator Based on Force Feedback for Decommissioning of Nuclear Facilities." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/9945758.

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To improve operation and reduce labor intensity during the process of decommissioning of nuclear facilities, a master-slave manipulation robot system based on force feedback for decommissioning of nuclear facilities is proposed, which is divided into three parts: master hand, wall-through pipe, and slave hand. The structure of master hand is designed according to the human-computer interaction design to add the perception of force information based on position perception. The master-slave manipulation system adopts a force-position hybrid control strategy; the position and force are sent to master and slave hand to discover telepresence operation. Incremental position control method realizes the workspace mapping between the master and the slave manipulator to improve the accuracy of the following performance. The novel zero-length spring compensation is designed to recognize the total gravity compensation of the force feedback device. Finally, the relative experiments have verified the work effectiveness of the master-slave manipulation system.

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Ananthanarayanan, S. P., A. A. Goldenberg, and J. Mylopoulos. "A qualitative theoretical framework for ‘common-sense’ based multiple contact robotic manipulation." Robotica 12, no. 2 (March 1994): 175–86. http://dx.doi.org/10.1017/s0263574700016751.

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SUMMARYThis paper presents a qualitative theoretical formulation for synthesis and analysis of multiple contact dexterous manipulation of an object, using a robot hand. The motivation for a qualitative theory is to build a formalisation of ‘human-like’ common-sense reasoning in robotic manipulation. Using this formalisation, a robot hand can perform finger-tip manipulative movements by analysing the physical laws that govern the robot hand, the object, and their interaction. Traditionally, such analysis have been framed in quantitative terms leading to mathematical systems which become intractable very quickly. Also, quantitative synthesis and analysis, often demand an accurate specification of the parameters in the universe of discourse, which is almost impossible to provide. The qualitative approach inherently encounters both these problems successfully.The qualitative theory is presented in three developmental stages. A qualitative framework of spatial information in the context of dexterous manipulation has been provided. Qualitative models of an object configuration and transformations in them that occur during a manipulation process, have been developed. Finally, the development of a ‘quasi-static’ qualitative framework of a dexterous manipulation process that performs the desired object transformation, has been presented.

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Torres-Jara, Eduardo, and Lorenzo Natale. "Sensitive Manipulation: Manipulation Through Tactile Feedback." International Journal of Humanoid Robotics 15, no. 01 (February 2018): 1850012. http://dx.doi.org/10.1142/s0219843618500123.

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Object grasping and manipulation in robotics has been largely approached using visual feedback. Human studies on the other hand have demonstrated the importance of tactile and force feedback to guide the interaction between the fingers and the objects. Inspired by these observations, we propose an approach that consists in guiding a robot’s actions mainly by tactile feedback, with remote sensing such as vision, used only as a complement. Directly sensing the interaction forces between the object, the environment, and the robot’s hand enables it to obtain information relevant to the task that can be used to perform it more reliably. This approach (that we call sensitive manipulation) requires important changes in the hardware and in the way the robot is programmed. At the hardware level, we exploit compliant actuators and novel sensors that allow to safely interact and detect the environment. We developed strategies to perform manipulation tasks that take advantage of these new sensing and actuation capabilities. In this paper, we demonstrate that using these strategies the humanoid robot Obrero can safely find, reach and grab unknown objects that are neither held in place by a fixture nor placed in a specific orientation. The robot can also make insertions by “feeling” the hole without specialized mechanisms such as a remote center of compliance (RCC).

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Speeter, Thomas H. "Transforming Human Hand Motion for Telemanipulation." Presence: Teleoperators and Virtual Environments 1, no. 1 (January 1992): 63–79. http://dx.doi.org/10.1162/pres.1992.1.1.63.

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Manipulation by teleoperation (telemanipulation) offers an apparently straightforward and less computationally expensive route toward dextrous robotic manipulation than automated control of multifingered robotic hands. The functional transformation of human hand motions into equivalent robotic hand motions, however, presents both conceptual and analytical problems. This paper reviews and proposes algorithmic methods for transforming the actions of human hands into equivalent actions of slave multifingered robotic hands. Forward positional transformation is considered only, the design of master devices, feedforward dynamics, and force feedback are not considered although their importance for successful telemanipulation is understood. Linear, nonlinear, and functional mappings are discussed along with performance and computational considerations.

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Leinen, Philipp, Matthew F. B. Green, Taner Esat, Christian Wagner, F. Stefan Tautz, and Ruslan Temirov. "Virtual reality visual feedback for hand-controlled scanning probe microscopy manipulation of single molecules." Beilstein Journal of Nanotechnology 6 (November 16, 2015): 2148–53. http://dx.doi.org/10.3762/bjnano.6.220.

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Controlled manipulation of single molecules is an important step towards the fabrication of single molecule devices and nanoscale molecular machines. Currently, scanning probe microscopy (SPM) is the only technique that facilitates direct imaging and manipulations of nanometer-sized molecular compounds on surfaces. The technique of hand-controlled manipulation (HCM) introduced recently in Beilstein J. Nanotechnol. 2014, 5, 1926–1932 simplifies the identification of successful manipulation protocols in situations when the interaction pattern of the manipulated molecule with its environment is not fully known. Here we present a further technical development that substantially improves the effectiveness of HCM. By adding Oculus Rift virtual reality goggles to our HCM set-up we provide the experimentalist with 3D visual feedback that displays the currently executed trajectory and the position of the SPM tip during manipulation in real time, while simultaneously plotting the experimentally measured frequency shift (Δf) of the non-contact atomic force microscope (NC-AFM) tuning fork sensor as well as the magnitude of the electric current (I) flowing between the tip and the surface. The advantages of the set-up are demonstrated by applying it to the model problem of the extraction of an individual PTCDA molecule from its hydrogen-bonded monolayer grown on Ag(111) surface.

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Takubo, Tomohito, Takeshi Nakamura, Riko Sugiyama, and Atsushi Ueno. "Multifunctional Shelf and Magnetic Marker for Stock and Disposal Tasks in Convenience Stores." Journal of Robotics and Mechatronics 35, no. 1 (February 20, 2023): 18–29. http://dx.doi.org/10.20965/jrm.2023.p0018.

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Product management using a multifunctional shelf, and manipulation using an electromagnet hand and a magnetic marker, are proposed for stock and disposal tasks. The multifunctional shelf manages the type, position, and number of products on the shelf, and plans display and disposal operations. The shelf provides directions to a mobile manipulator for moving products on the shelf according to the display and disposal plan. The proposed multifunctional shelf has a camera on each level that helps the mobile manipulator recognize the product. By optimizing the movement of products, the display and disposal work can be performed much more efficiently. To quickly grasp the product, a new manipulation strategy using a magnetic marker and an electromagnet hand is proposed. The electromagnet hand has two electromagnets and can quickly grasp and release the magnet marker by changing the S/N pole pair. Experiments using the proposed multifunctional shelf and electromagnet hand were conducted to demonstrate the effectiveness of the proposed system.

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Hu, Di, Cai-Hua Xiong, and Ming-Jin Liu. "Exploring the existence of better hands for manipulation than the human hand based on hand proportions." Journal of Theoretical Biology 440 (March 2018): 100–111. http://dx.doi.org/10.1016/j.jtbi.2017.12.026.

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Raja, Kavitha, Payal Katyal, and Saumen Gupta. "Assessment of in-hand manipulation: Tool development." International Journal of Health & Allied Sciences 5, no. 4 (2016): 235. http://dx.doi.org/10.4103/2278-344x.194092.

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Notoya, Masako, Shigetada Suzuki, Masayoshi Kurachi, Yoshiko Koyama, and Tamehisa Onoe. "Compulsive manipulation of objects with right hand." Higher Brain Function Research 5, no. 1 (1985): 764–70. http://dx.doi.org/10.2496/apr.5.764.

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MAEKAWA, Hitoshi, Makoto KANEKO, Kazuhito YOKOI, and Kazuo TANIE. "Position/Stiffness-based Manipulation by Multifingered Hand." Journal of the Robotics Society of Japan 9, no. 5 (1991): 580–91. http://dx.doi.org/10.7210/jrsj.9.580.

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OMATA, Toru. "Planning for manipulation by a multiffngered hand." Journal of the Robotics Society of Japan 9, no. 1 (1991): 85–91. http://dx.doi.org/10.7210/jrsj.9.85.

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Lokhande, Prof S. B. "Design and Manipulation of Robotic Prosthetic Hand." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 839–43. http://dx.doi.org/10.22214/ijraset.2020.6135.

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Odhner, Lael U., Leif P. Jentoft, Mark R. Claffee, Nicholas Corson, Yaroslav Tenzer, Raymond R. Ma, Martin Buehler, Robert Kohout, Robert D. Howe, and Aaron M. Dollar. "A compliant, underactuated hand for robust manipulation." International Journal of Robotics Research 33, no. 5 (February 17, 2014): 736–52. http://dx.doi.org/10.1177/0278364913514466.

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Chavan-Dafle, Nikhil, Rachel Holladay, and Alberto Rodriguez. "Planar in-hand manipulation via motion cones." International Journal of Robotics Research 39, no. 2-3 (October 17, 2019): 163–82. http://dx.doi.org/10.1177/0278364919880257.

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In this article, we present the mechanics and algorithms to compute the set of feasible motions of an object pushed in a plane. This set is known as the motion cone and was previously described for non-prehensile manipulation tasks in the horizontal plane. We generalize its construction to a broader set of planar tasks, such as those where external forces including gravity influence the dynamics of pushing, or prehensile tasks, where there are complex frictional interactions between the gripper, object, and pusher. We show that the motion cone is defined by a set of low-curvature surfaces and approximate it by a polyhedral cone. We verify its validity with thousands of pushing experiments recorded with a motion tracking system. Motion cones abstract the algebra involved in the dynamics of frictional pushing and can be used for simulation, planning, and control. In this article, we demonstrate their use for the dynamic propagation step in a sampling-based planning algorithm. By constraining the planner to explore only through the interior of motion cones, we obtain manipulation strategies that are robust against bounded uncertainties in the frictional parameters of the system. Our planner generates in-hand manipulation trajectories that involve sequences of continuous pushes, from different sides of the object when necessary, with 5–1,000 times speed improvements to equivalent algorithms.

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YANG, XIAOLI, and YOUN K. KIM. "HAND MANIPULATION TRAINING IN HAPTIC VIRTUAL ENVIRONMENTS." International Journal of Information Acquisition 05, no. 03 (September 2008): 269–81. http://dx.doi.org/10.1142/s021987890800165x.

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Continuing advances in virtual reality (VR) technology with respect to the new addition of force and touch feedbacks have enhanced VR realism and led to the development of many useful and accessible VR systems. One of the emerging research fields is in rehabilitation training. This paper introduces a virtual reality-based hand manipulation training system with three applications: virtual writing, virtual painting and virtual dialing. The system is mainly for training hand movement precision, speed, force, and direction control. A haptic device — PHANTOM Premium 1.0 is used to give the user immediate force feedbacks to feel immersed in the virtual environment during the training session. A new collision detection method is developed for accurate and rapid calculation of the interaction between the haptic and virtual environments. The implementation performances are calculated and given to the user in real time. The practicing results are also saved for evaluation and supervision by the specialist.

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YIN, Y. "MLD Modeling and MPC of Hand Manipulation." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 11 (November 1, 2005): 2999–3006. http://dx.doi.org/10.1093/ietfec/e88-a.11.2999.

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Fukano, R., T. Ootani, and Y. Kuniyoshi. "Statistical Learning for Manipulation by Robot Hand." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 93. http://dx.doi.org/10.1299/jsmermd.2004.93_3.

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Ichikura, Hinano, and Mitsuru Higashimori. "In-Hand Manipulation Inspired by Diabolo Juggling." IEEE Robotics and Automation Letters 7, no. 4 (October 2022): 12227–34. http://dx.doi.org/10.1109/lra.2022.3215066.

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Tetling, Christiane. "Hand drauf!" Deutsche Heilpraktiker-Zeitschrift 15, no. 03 (March 2020): 34–39. http://dx.doi.org/10.1055/a-1081-2530.

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SummaryTuina zählt als traditionelle chinesische Manualtechnik zu den fünf Säulen der TCM und reicht von muskulärer Massage über Faszienbehandlung bis hin zu Akupressur und chiropraktischer Manipulation. Entsprechend der TCM-Diagnose soll die Zirkulation von Qi und Blut reguliert werden, wobei Techniken wie Reiben, Schieben, Pressen, Kneten oder Kneifen zur Anwendung kommen. Die Behandlung eignet sich insbesondere bei Schmerzen, muskulären Verspannungen, Menstruations-, Verdauungs- und Schlafstörungen sowie zur Entspannung.

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Murata, A., V. Gallese, M. Kaseda, and H. Sakata. "Parietal neurons related to memory-guided hand manipulation." Journal of Neurophysiology 75, no. 5 (May 1, 1996): 2180–86. http://dx.doi.org/10.1152/jn.1996.75.5.2180.

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1. We recorded activity of the hand-manipulation-task-related neurons in the posterolateral bank of the anterior intraparietal sulcus (area AIP) of the monkey parietal cortex during a delayed hand manipulation task. 2. We examined mainly the object-type visual-dominant and visual-and-motor neurons that responded to the sight of objects for manipulation. The majority of these neurons (32 of 48) showed sustained activity during the delay period in the dark before manipulation of preferred objects. 3. Six visual-and-motor neurons showed set-related activity before the hand manipulation in the dark, so that their delay period activity was likely to be related to motor preparation. 4. The delay period activity of 18 visual-dominant and visual-and-motor neurons without set-related activity was likely to represent spatial features of objects, because the majority of the neurons showed the same selectivity in the shape and/or orientation during object fixation and the delay period. 5. Of these 18 neurons, 10 showed sustained activity in the dark after brief illumination of objects during a light-interrupted fixation task, suggesting that they store the short-term memory of objects without the intention to remember. 6. The results suggest that the visual memory of three-dimensional features of objects is likely to be incorporated in area AIP and to be used for the guidance of hand manipulation.

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Mesuda, Yuko, Shigeru Inui, and Yosuke Horiba. "Virtual manipulations for draping." International Journal of Clothing Science and Technology 27, no. 3 (June 1, 2015): 417–33. http://dx.doi.org/10.1108/ijcst-10-2013-0119.

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Purpose – Draping is one method used in clothing design. It is important to virtualize draping in real time, and virtual cloth handling is a key technology for this purpose. A mouse is often used for real-time cloth handling in many studies. However, gesture manipulation is more realistic than movements using the mouse. The purpose of this paper is to demonstrate virtual cloth manipulation using hand gestures in the real world. Design/methodology/approach – In this study, the authors demonstrate three types of manipulation: moving, cutting, and attaching. The user’s hand coordinates are obtained with a Kinect, and the cloth model is manipulated by them. The cloth model is moved based on the position of the hand coordinates. The cloth model is cut along a cut line calculated from the hand coordinates. In attaching the cloth model, it is mapped to a dummy model and then part of the cloth model is fixed and another part is released. Findings – This method can move the cloth model according to the motion of the hands. The authors have succeeded in cutting the cloth model based on the hand trajectory. The cloth model can be attached to the dummy model and its form is changed along the dummy model shape. Originality/value – Cloth handling in many studies is based on indirect manipulation using a mouse. In this study, the cloth model is manipulated according to hand motion in the real world in real time.

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