Dimensionality reduction for hand-independent dexterous robotic grasping

Ciocarlie, Matei; Goldfeder, Corey; Allen, Peter K.

doi:10.1109/iros.2007.4399227

Cited by 190 publications

(157 citation statements)

References 16 publications

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“…to characterize the configurations of the hand used to grasp different objects. This subspace is used in other works to look for grasping configurations [17], [18]. As a difference with these works, we use here an initial set of unconstrained general hand configurations in order to model all the real hand workspace and not only potential grasping configurations.…”

Section: Problem Statement and Solution Overviewmentioning

confidence: 99%

Motion planning for high DOF anthropomorphic hands

Rosell

Suárez

Rosales

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions. The proposed approach tries to mimic the real human hand motions, but reducing the dimension of the search space in order to obtain results as a compromise between motion optimality and planning complexity (time) by means of the concept of principal motion directions. Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a sampling-based roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand, and some examples are included to illustrate its validity.

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Section: Problem Statement and Solution Overviewmentioning

confidence: 99%

Motion planning for high DOF anthropomorphic hands

Rosell

Suárez

Rosales

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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“…Nevertheless we want to extend the idea of to create a learning system based on the fusion between the concept of key posture and EigenGrasp, presented in [6].…”

Section: Resultsmentioning

confidence: 99%

Key Posture Extraction from Object Manipulations Experiments

Melo

Watanabe

2011

AEF

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Abstract. In this work we present a novel concept for key posture by looking for grasping similarities among several grasping experiments. To highlight the nature of key posture idea, the object used in experiment had different sizes, although share the same type. Grouping the extracted data by joint, we search for time interval with high data concentration. If this time interval is shared by many joints in the same experiment we can extract key posture from that interval. The key posture can help a robotic hand system to grasp, control and manipulate the object through a specific task.

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“…The results presented in [14] show that, in general, postures where the hand conforms perfectly to the surface of the target can not be found in eigengrasp space. However, by searching this subspace we can usually find a posture that is very close to a desired grasp.…”

Section: Low-dimensional Grasp Planningmentioning

confidence: 92%

“…By choosing a basis comprising b eigengrasps, a hand posture p placed in the subspace defined by this basis is uniquely defined by the vector a ∈ R b containing the amplitudes along each subspace axis. In previous work [14], we have discussed the feasibility of finding good grasps for dexterous hands by searching a subspace defined by two eigengrasps. This implies a significant dimensionality reduction of the grasp quality function domain, which can be expressed as…”

Section: Low-dimensional Grasp Planningmentioning

confidence: 99%

On-Line Interactive Dexterous Grasping

Ciocarlie

Allen

2008

Haptics: Perception, Devices and Scenarios

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Abstract. In this paper we describe a system that combines human input and automatic grasp planning for controlling an artificial hand, with applications in the area of hand neuroprosthetics. We consider the case where a user attempts to grasp an object using a robotic hand, but has no direct control over the hand posture. An automated grasp planner searches for stable grasps of the target object and shapes the hand accordingly, allowing the user to successfully complete the task. We rely on two methods for achieving the computational rates required for effective user interaction: first, grasp planning is performed in a hand posture subspace of highly reduced dimensionality; second, our system uses real-time input provided by the human user, further simplifying the search for stable grasps to the point where solutions can be found at interactive rates. We demonstrate our approach on a number of different hand models and target objects, in both real and virtual environments.

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Dimensionality reduction for hand-independent dexterous robotic grasping

Cited by 190 publications

References 16 publications

Motion planning for high DOF anthropomorphic hands

Motion planning for high DOF anthropomorphic hands

Key Posture Extraction from Object Manipulations Experiments

On-Line Interactive Dexterous Grasping

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