Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis

Brown, Christopher Y.; Asada, H. Harry

doi:10.1109/iros.2007.4399547

Cited by 188 publications

(130 citation statements)

References 4 publications

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“…tasks [4], [9], [10]. Switching between discrete modes of operation rather than fine control over the transmission ratio and requiring an additional source of actuation are two of the main limitations in the proposed methods.…”

Section: Introductionmentioning

confidence: 99%

Stiffness Control With Shape Memory Polymer in Underactuated Robotic Origamis

2017

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Abstract-Under-actuated systems offer compact designs with easy actuation and control but at the cost of limited stable configurations and reduced dexterity compared to the directly driven and fully actuated systems. Here, we propose a compact origami-based design to control the stable configurations and the overall stiffness of an under-actuated robotic finger by modulating the material stiffness of the joint. The design of the robotic finger is based on the robotic origami design principle in which multiple functional layers are integrated to make a nominally 2D robot with a desired functionality. To control the stiffness of the structure, we controlled the elastic modulus of a shape memory polymer (SMP) via embedded customized stretchable heater. We monitor the configuration of the finger using the feedback from the customized curvature sensors embedded in each joint. We studied the stable configurations and the contact forces of a finger with 3 joints at different stiffness settings. A scaled down version of the design was used in a gripper with two fingers and different grasp modes were demonstrated through activating different set of joints.

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Section: Introductionmentioning

confidence: 99%

Stiffness Control With Shape Memory Polymer in Underactuated Robotic Origamis

2017

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“…In previous work [11], we have applied this concept to five hand models, using the results of Santello et al for the anthropomorphic models (such as the DLR and Robonaut hands) and empirically derived subspaces for the non-anthropomorphic ones (such as the Barrett hand). Brown and Asada [12] also present an anthropomorphic robotic hand with a low-dimensional control system along directions similar to those presented in [10]. A discussion on different dimensionality reduction techniques for hand control is presented by Peters and Jenkins [13], applied to teleoperation data on a Robonaut hand.…”

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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“…This approach differs from other works where the synergies are considered perfectly stiff and the actual joint variables are a linear combination of synergies [21], [22]. In a compliant actuation model, the torques applied by the joint motors is proportional to the difference between the reference and the actual joint displacement…”

Section: A Synergy Backgroundmentioning

confidence: 99%

An Object-Based Approach to Map Human Hand Synergies onto Robotic Hands with Dissimilar Kinematics

Gioioso¹,

Salvietti²,

Malvezzi³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-Robotic hands differ in kinematics, dynamics, programming, control and sensing frameworks. Borrowing the terminology from software engineering, there is a need for middleware solutions to control the robotic hands independently from their specific structure, and focusing only on the task.Results in neuroscience concerning the synergistic organization of the human hand, are the theoretical foundation of this work, which focuses on the problem of mapping human hand synergies on robotic hands with dissimilar kinematic structures.The proposed mapping is based on the use of a virtual ellipsoid and it is mediated by a model of an anthropomorphic robotic hand able to capture the idea of synergies in human hands. This approach has been tested in two different robotic hands with an anthropomorphic and non-anthropomorphic kinematic structure.

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Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis

Cited by 188 publications

References 4 publications

Stiffness Control With Shape Memory Polymer in Underactuated Robotic Origamis

Stiffness Control With Shape Memory Polymer in Underactuated Robotic Origamis

On-Line Interactive Dexterous Grasping

An Object-Based Approach to Map Human Hand Synergies onto Robotic Hands with Dissimilar Kinematics

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