
This paper proposes an iterative learning control (ILC) scheme 
for a class of redundant robot arms to acquire the desired control 
input signals that produce an endpoint trajectory specified 
in task space. The learning update law of control input signals 
is constructed only in task space by modifying the previous control 
input through adding linearly an endpoint velocity trajectory error.  
Although the dimension of the task space is strictly less than 
the DOF (Degrees-of-freedom) of the robot arm, the proposed method 
need neither consider any inverse kinematics problem nor introduce 
any cost function to be optimized and to determine the inverse kinematics 
(or dynamics) uniquely. In this movie, convergence of trajectory trackings 
to the specified one is shown by numerical simulations in both cases 
1) free-endpoint motion and 2) constraint-endpoint motion with specified 
contact force.  

