Mechanisms of the Anatomically Correct Testbed Hand

Deshpande, Ashish D.; Xu, Zhe; Weghe, Michael J. Vande; Brown, Bryan L.; Ko, Jonathan; Chang, Lillian Y.; Wilkinson, David; Bidic, S. M.; Matsuoka, Yoky

doi:10.1109/tmech.2011.2166801

Cited by 144 publications

(71 citation statements)

References 46 publications

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“…High number of actuated dof in a small space makes it hard to design such manipulators. Dexterous robotic hands exist [1] [2] [3], but the difficulty in controlling them has motivated many researchers to focus on more limited mechanisms. Our goal instead is to develop general-purpose control solutions that can be applied to mechanisms as complex as a human hand.…”

Section: Introductionmentioning

confidence: 99%

Real-time behaviour synthesis for dynamic hand-manipulation

Kumar

Tassa

Erez

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Dexterous hand manipulation is one of the most complex types of biological movement, and has proven very difficult to replicate in robots. The usual approaches to robotic control -following pre-defined trajectories or planning online with reduced models -are both inapplicable. Dexterous manipulation is so sensitive to small variations in contact force and object location that it seems to require online planning without any simplifications. Here we demonstrate for the first time online planning (or model-predictive control) with a full physics model of a humanoid hand, with 28 degrees of freedom and 48 pneumatic actuators. We augment the actuation space with motor synergies which speed up optimization without removing dexterity. Most of our results are in simulation, showing nonprehensile object manipulation as well as typing. In both cases the input to the system is a high level task description, while all details of the hand movement emerge online from fully automated numerical optimization.

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Section: Introductionmentioning

confidence: 99%

Real-time behaviour synthesis for dynamic hand-manipulation

Kumar

Tassa

Erez

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…The following subsections describe the mechanical components in the ACT Hand, including its finger bones, joints, tendons and actuators [15].…”

Section: Background On Human Handsmentioning

confidence: 99%

Development of an Anatomically Correct Testbed (ACT) Hand

Deshpande

Matsuoka

2014

Springer Tracts in Advanced Robotics

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We have built an Anatomically Correct Testbed (ACT) hand with the purpose of understanding the intrinsic biomechanical and control features of the human hands that are critical for achieving robust, versatile, and dexterous movements as well as object and world exploration. By mimicking the underlying mechanics and controls of the human hand in a hardware platform, our goal is to achieve previously unmatched grasping and manipulation capabilities. In this chapter we present distinguishing design philosophy and features of the ACT Hand compared to the existing robotic hands, and the details of the design and assembly of the finger bones, joints, tendons and actuators. We derive and analyze the unique muscle-to-joint relationships, called the moment arms, in the ACT Hand index finger, and present a software architecture for the control of the hand movement and forces by controlling the numerous muscle actuators. We also illustrate the grasping and manipulation abilities of the ACT Hand. The fully functional ACT Hand platform allows us to experiment with novel control algorithms to develop a deeper understanding of human dexterity.keywords Robotic hand Á Hand biomechanics Á Human-inspired design Á Hand control software Á Moment arm variations

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“…The robotic hand mimics the interaction among muscle excursions and joint movements produced by the bone and tendon geometries of the human hand, as in [6]. The index finger has the full 4 degree-of-freedom joint mobility and is controlled by six motor-driven tendons acting through a crocheted tendon-hood.…”

Section: Tendon-driven Biomimetic Handmentioning

confidence: 99%

Tendon-Driven Variable Impedance Control Using Reinforcement Learning

Malhotra¹,

Theodorou²,

Matsuoka³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-Biological motor control is capable of learning complex movements containing contact transitions and unknown force requirements while adapting the impedance of the system. In this work, we seek to achieve robotic mimicry of this compliance, employing stiffness only when it is necessary for task completion. We use path integral reinforcement learning which has been successfully applied on torque-driven systems to learn episodic tasks without using explicit models. Applying this method to tendon-driven systems is challenging because of the increase in dimensionality, the intrinsic nonlinearities of such systems, and the increased effect of external dynamics on the lighter tendon-driven end effectors.We demonstrate the simultaneous learning of feedback gains and desired tendon trajectories in a dynamically complex slidingswitch task with a tendon-driven robotic hand. The learned controls look noisy but nonetheless result in smooth and expert task performance. We show discovery of dynamic strategies not explored in a demonstration, and that the learned strategy is useful for understanding difficult-to-model plant characteristics.

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Mechanisms of the Anatomically Correct Testbed Hand

Cited by 144 publications

References 46 publications

Real-time behaviour synthesis for dynamic hand-manipulation

Real-time behaviour synthesis for dynamic hand-manipulation

Development of an Anatomically Correct Testbed (ACT) Hand

Tendon-Driven Variable Impedance Control Using Reinforcement Learning

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