Letters to Nature

Nature 414, 446-449 (22 November 2001) | doi:10.1038/35106566; Received 8 June 2001; Accepted 27 September 2001

The central nervous system stabilizes unstable dynamics by learning optimal impedance

Etienne Burdet1,2,4,5, Rieko Osu2,5, David W. Franklin4,6, Theodore E. Milner6 & Mitsuo Kawato2,4

  1. Department of Mechanical Engineering, National University of Singapore, 119260, Singapore
  2. Kawato Dynamic Brain Project, ERATO, JST, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619 0288, Japan
  3. ATR Human Information Science Laboratories, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619 0288, Japan
  4. School of Kinesiology, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
  5. These authors contributed equally to the work

Correspondence to: Mitsuo Kawato2,4 Correspondence and requests for materials should be addressed to M.K. (e-mail: Email: kawato@atr.co.jp). Further information about this work can be found at http://www.bioeng.nus.edu.sg/research.htm.

To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics1, 2, 3, 4, 5, 6, that is, a neural representation of the relation between motor command and movement5, 7. In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable8, 9. For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.