Skip to main content

Advertisement

Springer Nature Link
Log in

The Mechanisms in a Humanoid Robot Hand

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

We have constructed and tested a robotic hand that is a part of a humanoid being developed at the MIT Artificial Intelligence Laboratory. It is a human-scale cable-driven tool containing four actuators, thirty-six sensors, and computing tools on board. The combination of its conciseness and sensing capability allows an integration with other anthropomorphically scaled systems. This paper presents a detailed description of the mechanical design and its implementation, including the structure of the physical hand, tendon cabling strategy, actuators, sensors and computing tools.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Bekey, A., Tomovic, R., and Zeljkovic, I. 1990. Control architecture for the Belgrade/USC hand. Dextrous Robotic Hands, Springer-Verlag: New York. pp. 136-149.

    Google Scholar 

  • Brock, D.L. and Salisbury, J.K. 1991. Implementation of Behavioral Control on a Robot Hand/Aarm System.

  • Brooks, R.A. 1994. L., IS Robotics, Inc.

  • Brooks, R.A. and Stein, L.A. 1994. Building brains for bodies. Autonomous Robots, 1:7-25.

    Google Scholar 

  • Cutkosky, M.R. and Howe, R.D. Dextrous Robot Hands.

  • Ferrell, C. 1996. Orientation behavior using registered topographic maps. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp. 94-103.

  • Franklin, G.F, Powell, J.D., and Emami-Naeini, A. 1991. Feedback Control of Dynamic Systems, Addison Wesley Publishing company.

  • Greiner, H. 1990. Passive and active grasping with a prehensile robot end-effector. MIT A.I. Lab, Cambridge, MA. S.M. Thesis.

    Google Scholar 

  • Heykin, S. 1994. Neural Networks, Macmillan College Publishing Company: New York, NY.

    Google Scholar 

  • Irie, R.E. 1995. Robust Sound Localization: An application of an auditory perception system for a humanoid robot. M.I.T. Department of Electrical Engineering and Computer Science, Cambridge, MA. S.M. Thesis.

    Google Scholar 

  • Jacobson, S.C. et al. 1984. The Utah/MIT dextrous hand: Work in progress. The first International Symposium of Robotics Research.

  • Kapogiannis, E. 1994. Design of a large scale MIMD computer. M.I.T. Department of Electrical Engineering and Computer Science, Cambridge, MA. M.Eng. Thesis.

    Google Scholar 

  • Kernodle, M.W. and Carlton, L.G. 1992. Information feedback and the learning of multiple-degree-of-freedom activities. Journal of Motor Behavior, 24(2):187-196.

    Google Scholar 

  • Kohonen, T. 1982. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43:59-69.

    Google Scholar 

  • Lin, L. 1992. Reinforcement learning for robots using neural networks. Carnegie Mellon University, Pittsburgh, PA. Ph.D. Thesis.

    Google Scholar 

  • Lundstrom, G., Glemme, B., and Rooks, B.W. 1979. Industrial Robots-GRIPPER REVIEW. International Fluidics Services Ltd.

  • Marjanović, M. 1995. Learning functional maps between sensorimotor systems on a humanoid Robot. M.I.T. Department of Electrical Engineering and Computer Science, Cambridge MA. S.M. Thesis.

    Google Scholar 

  • Marjanović, M., Scassellatti, B., and Williamson, M. 1996. Self-taught visually-guided pointing for a humanoid robot. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp 35-44.

  • Matsuoka, Y. 1995. Embodiment and manipulation learning process for a humanoid hand. M.I.T. Department of Electrical Engineering and Computer Science, Cambridge, MA. S.M. Thesis.

    Google Scholar 

  • Matsuoka, Y. 1997. Manipulative exploratory behavior with embodied hand. Adaptive Behavior. To appear.

  • Salisbury, J.K. 1982. Kinematic and force analysis of articulated hands. Stanford University Mechanical Engineering and Computer Science Dept., Stanford, CA, Ph.D. Thesis.

    Google Scholar 

  • Salisbury, J.K. and Craig, J.J. 1982. Articulated hands: Force control and kinematic issues. International Journal of Robotics Research, 1:1.

    Google Scholar 

  • Shirane, R. 1985. Tsukuba kagakuhaku to nihon no kagaku gijutsu. Journal of the Robotics Society of Japan, 4(4):42.

    Google Scholar 

  • von Hofsten, C. 1991. Structuring of early reaching movements: A longitudinal study. Journal of Motor Behavior., 23(4):280-292.

    Google Scholar 

  • Wiener, N. 1948. Cybernetics, MIT Press: Cambridge, MA.

    Google Scholar 

  • Williamson, M. 1995. Series elastic actuators. M.I.T. Department of Electrical Engineering and Computer Science, Cambridge, MA. S.M. Thesis.

    Google Scholar 

  • Williamson, M. 1996. Postural Primtiives: Interactive behavior for a humanoid robot arm. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp. 124-134.

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence Lab, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, MA, 02139, U.S.A.

    Yoky Matsuoka

Authors
  1. Yoky Matsuoka
    View author publications

    You can also search for this author inPubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Matsuoka, Y. The Mechanisms in a Humanoid Robot Hand. Autonomous Robots 4, 199–209 (1997). https://doi.org/10.1023/A:1008873918987

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008873918987