  Fast and precise motion control is important for industrial robots in
manufacturing applications. However, some collaborative robots sacrifice
precision for safety, particular for high motion speed. The performance
degradation is caused by the inability of the joint servo controller to address
the uncertain nonlinear dynamics of the robot arm, e.g., due to joint
flexibility. We consider two approaches to improve the trajectory tracking
performance through feedforward compensation. The first approach uses iterative
learning control, with the gradient-based iterative update generated from the
robot forward dynamics model. The second approach uses dynamic inversion to
directly compensate for the robot forward dynamics. If the forward dynamics is
strictly proper or is non-minimum-phase (e.g., due to time delays), its stable
inverse would be non-causal. Both approaches require robot dynamical models.
This paper presents results of using recurrent neural networks (RNNs) to
approximate these dynamical models-forward dynamics in the first case, inverse
dynamics (possibly non-causal) in the second case. We use the bi-directional
RNN to capture the noncausality. The RNNs are trained based on a collection of
commanded trajectories and the actual robot responses. We use a Baxter robot to
evaluate the two approaches. The Baxter robot exhibits significant joint
flexibility due to the series-elastic joint actuators. Both approaches achieve
sizable improvement over the uncompensated robot motion, for both random joint
trajectories and Cartesian motion. The inverse dynamics method is particularly
attractive as it may be used to more accurately track a user input as in
teleoperation.
