The central nervous system stabilizes unstable dynamics by learning optimal impedance

Etienne Burdet, Rieko Osu, David W. Franklin, Theodore E. Milner, Mitsuo Kawato

Research output: Contribution to journalArticlepeer-review

842 Citations (Scopus)


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 dynamics1-6, 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.

Original languageEnglish
Pages (from-to)446-449
Number of pages4
Issue number6862
Publication statusPublished - 2001 Nov 22
Externally publishedYes

ASJC Scopus subject areas

  • General


Dive into the research topics of 'The central nervous system stabilizes unstable dynamics by learning optimal impedance'. Together they form a unique fingerprint.

Cite this