  With the accelerated development of robot technologies, optimal control
becomes one of the central themes of research. In traditional approaches, the
controller, by its internal functionality, finds appropriate actions on the
basis of the history of sensor values, guided by the goals, intentions,
objectives, learning schemes, and so on planted into it. The idea is that the
controller controls the world---the body plus its environment---as reliably as
possible. However, in elastically actuated robots this approach faces severe
difficulties. This paper advocates for a new paradigm of self-organized
control. The paper presents a solution with a controller that is devoid of any
functionalities of its own, given by a fixed, explicit and context-free
function of the recent history of the sensor values. When applying this
controller to a muscle-tendon driven arm-shoulder system from the Myorobotics
toolkit, we observe a vast variety of self-organized behavior patterns: when
left alone, the arm realizes pseudo-random sequences of different poses but one
can also manipulate the system into definite motion patterns. But most
interestingly, after attaching an object, the controller gets in a functional
resonance with the object's internal dynamics: when given a half-filled bottle,
the system spontaneously starts shaking the bottle so that maximum response
from the dynamics of the water is being generated. After attaching a pendulum
to the arm, the controller drives the pendulum into a circular mode. In this
way, the robot discovers dynamical affordances of objects its body is
interacting with. We also discuss perspectives for using this controller
paradigm for intention driven behavior generation.
