  A robot's dynamics -- especially the degree and location of compliance -- can
significantly affect performance and control complexity. Passive dynamics can
be designed with good regions of attraction or limit cycles for a specific
task, but achieving flexibility on a range of tasks requires co-design of
control. This paper takes an information perspective: the robot dynamics should
reduce the amount of information required for a controller to achieve a
threshold of performance in a range of tasks. Towards this goal, an iterative
method is proposed to minimize the directed information from state to control
on discrete-time nonlinear systems. iLQG is used to find a controller and value
of information, then the design parameters of the dynamics (e.g. stiffness of
end-effector or joint) are optimized to reduce directed information while
maintaining a minimum bound on performance. The approach is validated in
simulation, on a two-mass system in contact with an uncertain wall position and
a high-DOF door opening task, and shown to improve noise robustness and reduce
time variance of control gains.
