By Topic

Whole body humanoid control from human motion descriptors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Dariush, B. ; Honda Res. Inst., Mountain View, CA ; Gienger, M. ; Bing Jian ; Goerick, C.
more authors

Many advanced motion control strategies developed in robotics use captured human motion data as valuable source of examples to simplify the process of programming or learning complex robot motions. Direct and online control of robots from observed human motion has several inherent challenges. The most important may be the representation of the large number of mechanical degrees of freedom involved in the execution of movement tasks. Attempting to map all such degrees of freedom from a human to a humanoid is a formidable task from an instrumentation and sensing point of view. More importantly, such an approach is incompatible with mechanisms in the central nervous system which are believed to organize or simplify the control of these degrees of freedom during motion execution and motor learning phase. Rather than specifying the desired motion of every degree of freedom for the purpose of motion control, it is important to describe motion by low dimensional motion primitives that are defined in Cartesian (or task) space. In this paper, we formulate the human to humanoid retargeting problem as a task space control problem. The control objective is to track desired task descriptors while satisfying constraints such as joint limits, velocity limits, collision avoidance, and balance. The retargeting algorithm generates the joint space trajectories that are commanded to the robot. We present experimental and simulation results of the retargeting control algorithm on the Honda humanoid robot ASIMO.

Published in:

Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on

Date of Conference:

19-23 May 2008