  In robotics, catastrophic interference continues to restrain policy training
across environments. Efforts to combat catastrophic interference to date focus
on novel neural architectures or training methods, with a recent emphasis on
policies with good initial settings that facilitate training in new
environments. However, none of these methods to date have taken into account
how the physical architecture of the robot can obstruct or facilitate
catastrophic interference, just as the choice of neural architecture can. In
previous work we have shown how aspects of a robot's physical structure
(specifically, sensor placement) can facilitate policy learning by increasing
the fraction of optimal policies for a given physical structure. Here we show
for the first time that this proxy measure of catastrophic interference
correlates with sample efficiency across several search methods, proving that
favorable loss landscapes can be induced by the correct choice of physical
structure. We show that such structures can be found via co-optimization --
optimization of a robot's structure and control policy simultaneously --
yielding catastrophic interference resistant robot structures and policies, and
that this is more efficient than control policy optimization alone. Finally, we
show that such structures exhibit sensor homeostasis across environments and
introduce this as the mechanism by which certain robots overcome catastrophic
interference.
