Anatomically Correct Testbed
Hand Control: Muscle and Joint Control Strategies
Proc. of the International Conference on Robotics
and Automation (ICRA), 2009
Abstract
Human hands are capable of many dexterous grasping and
manipulation tasks. To understand human levels of dexterity and to
achieve it with robotic hands, we constructed an anatomically correct
testbed (ACT) hand to investigate the biomechanical features and
neural control strategies of the human hand. This paper focuses on
developing control strategies for the index finger motion of the ACT
Hand. A direct muscle position control and a force-optimized joint
control are implemented as building blocks and tools for comparisons
with future biological control approaches. We show how Gaussian
process regression techniques can be used for nonlinear parameter
estimation in both controllers. Our experiments demonstrate that the
direct muscle position controller allows for accurate and fast
position tracking, while the force-optimized joint controller allows
for exploitation of actuation redundancy in the finger critical for
this redundant system. Furthermore, a thogrough comparison between
Gaussian processes and polynomials for non-linear regression shows
that Gaussian processes provide significantly better parameter
estimation and tracking performance. This first control investigation
on the ACT hand opens doors to implement biological strategies
observed in humans and achieve the ultimate human-level dexterity.