Precision grasp synergies for dexterous robotic hands

Bernardino, Alexandre; Henriques, Marco Aurélio Amaral; Hendrich, Norman; Zhang, Jianwei

doi:10.1109/robio.2013.6739436

Cited by 26 publications

(19 citation statements)

References 16 publications

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“…Most of the research in this field has been dedicated to rigid body grasping [1] [2] [3] [4] [5] [6], and only a few studies have considered the case of deformable objects.…”

Section: Introductionmentioning

confidence: 99%

Model-based strategy for grasping3Ddeformable objects using a multi-fingered robotic hand

Zaidi

Ramón

Bouzgarrou

et al. 2017

Robotics and Autonomous Systems

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. Model-based strategy for grasping 3 D deformable objects using a multifingered robotic hand.Robotics and Autonomous Systems, Elsevier, 2017, 95, pp.196-206 AbstractThis paper presents a model-based strategy for 3D deformable object grasping using a multi-fingered robotic hand. The developed contact model is based on two force components (normal force and tangential friction force, including slipping and sticking effects) and uses a non-linear mass-spring system to describe the object deformations due the mechanical load applied by the fingers of the robotic hand. The object-finger interaction is simulated in order to compute the required contact forces and deformations to robustly grasp objects with large deformations. Our approach is able to achieve this by using a non-linear model that outperforms current techniques that are limited to using linear models. After the contact forces computed by the simulation of the contact model guarantee the equilibrium of the grasp, they will be used as set-points for forcecontrolling the closing of the real fingers, and thus, the proposed grasping strategy is implemented. Two different objects (cube and sphere) made from two soft materials (foam and rubber) are tested in order to verify that the proposed model can represent their non-linear deformations and that the proposed grasp strategy can implement a robust grasp of them with a multi-fingered robotic hand equipped with tactile sensors. Thereby, both the grasping strategy and the proposed contact model are validated experimentally.

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“…Most of the research in this field has been dedicated to rigid body grasping [1] [2] [3] [4] [5] [6], and only a few studies have considered the case of deformable objects.…”

Section: Introductionmentioning

confidence: 99%

Model-based strategy for grasping3Ddeformable objects using a multi-fingered robotic hand

Zaidi

Ramón

Bouzgarrou

et al. 2017

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…While a manipulation task usually involves multifinger movement, a human can only operate one finger with each hand and hence two fingers simultaneously at most. To control multi-finger hands, some researchers use tele-operation (Bernardino et al, 2013;Kondo et al, 2008;Fischer et al, 1998). This usually relies on data gloves or other motion-capture systems, which sense the human hand and arm motions.…”

Section: Learning Manipulation Tasksmentioning

confidence: 99%

A modular approach to learning manipulation strategies from human demonstration

Huang

Souza

et al. 2015

Auton Robot

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Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strat-

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“…A limitation of these works is the need to maintain direct visual contact with the task space where the robot is operating. This is also true when we tele-operate the hands of a robot, for example in [8] where sensor gloves are used to control the hand of the iCub robot. This limitation can be overcome by providing the operator with a firstperson-view [9].…”

Section: Introductionmentioning

confidence: 99%

First-person tele-operation of a humanoid robot

Fritsche

Unverzag

Peters

et al. 2015

2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

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Abstract-Remote control of robots is often necessary to complete complex unstructured tasks in environments that are inaccessible (e.g. dangerous) for humans. Tele-operation of humanoid robots is often performed trough motion tracking to reduce the complexity deriving from manually controlling a high number of DOF. However, most commercial motion tracking apparatus are expensive and often uncomfortable. Moreover, a limitation of this approach is the need to maintain visual contact with the operated robot, or to employ a second human operator to independently maneuver a camera. As a result, even performing simple tasks heavily depends on the skill and synchronization of the two operators. To alleviate this problem we propose to use augmented-reality to provide the operator with first-person vision and a natural interface to directly control the camera, and at the same time the robot. By integrating recent off-the-shelf technologies, we provide an affordable and intuitive environment composed of Microsoft Kinect, Oculus Rift and haptic SensorGlove to tele-operate in first-person humanoid robots. We demonstrate on the humanoid robot iCub that this set-up allows to quickly and naturally accomplish complex tasks.

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Precision grasp synergies for dexterous robotic hands

Cited by 26 publications

References 16 publications

Model-based strategy for grasping3Ddeformable objects using a multi-fingered robotic hand

Model-based strategy for grasping3Ddeformable objects using a multi-fingered robotic hand

A modular approach to learning manipulation strategies from human demonstration

First-person tele-operation of a humanoid robot

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