A Framework for Grasp Simulation and Control in Domestic Environments

Tegin, Johan; Wikander, Jan

doi:10.3182/20060912-3-de-2911.00086

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…In this paper, a weighted sum of the contact forces, finger position difference (stability), off-set (centering), and finger spread is controlled (see Fig. 3 and [29] for more detail). The transform T used in this paper is…”

Section: Grasp Controlmentioning

confidence: 99%

Demonstration-based learning and control for automatic grasping

Tegin¹,

Ekvall²,

Kragić³

et al. 2008

Intel Serv Robotics

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We present a method for automatic grasp generation based on object shape primitives in a Programming by Demonstration framework. The system first recognizes the grasp performed by a demonstrator as well as the object it is applied on and then generates a suitable grasping strategy on the robot. We start by presenting how to model and learn grasps and map them to robot hands. We continue by performing dynamic simulation of the grasp execution with a focus on grasping objects whose pose is not perfectly known.

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Section: Grasp Controlmentioning

confidence: 99%

Demonstration-based learning and control for automatic grasping

Tegin¹,

Ekvall²,

Kragić³

et al. 2008

Intel Serv Robotics

Self Cite

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show abstract

“…However, this can be considered as a separate problem and is outside the scope of this study. Another way of improving performance is to add a more advanced controller, such as the one presented in [22]. Here, the estimated pose combined with tactile sensing is used as feedback to adjust the forces applied at the finger joints.…”

Section: Grasp Controllersmentioning

confidence: 99%

Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning

Ekvall

Kragić

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-In this paper, we address the problem of automatic grasp generation for robotic hands where experience and shape primitives are used in synergy so to provide a basis not only for grasp generation but also for a grasp evaluation process when the exact pose of the object is not available. One of the main challenges in automatic grasping is the choice of the object approach vector, which is dependent both on the object shape and pose as well as the grasp type. Using the proposed method, the approach vector is chosen not only based on the sensory input but also on experience that some approach vectors will provide useful tactile information that finally results in stable grasps. A methodology for developing and evaluating grasp controllers is presented where the focus lies on obtaining stable grasps under imperfect vision. The method is used in a teleoperation or a Programming by Demonstration setting where a human demonstrates to a robot how to grasp an object. The system first recognizes the object and grasp type which can then be used by the robot to perform the same action using a mapped version of the human grasping posture.

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“…In our work, we do not consider the control paradigm but we correct grasps based on prior experience. The work of [12] did not consider the success of a grasp before executing the controller.…”

Section: Related Workmentioning

confidence: 99%

“…[11] used a programming by demonstration approach where a robot relies on human-to-robot grasp mapping to learn most likely hand preshapes for specific objects. Starting from the inferred preshape, a force controller that relied on joint angles and tactile sensing [12] was selected to handle position and orientation uncertainty. A grasp was chosen via control laws and experience was only used to select the hand preshapes for the given object.…”

Section: Related Workmentioning

confidence: 99%