Self-Organized Behavior Generation for Musculoskeletal Robots

Abstract

With the accelerated development of robot technologies, 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 specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors “waiting” to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics.

Embodied robots driven by self-organized environmental feedback

“…Incorporating objects of the environment into the overarching dynamical system allows in consequence dynamical system approaches also to classify computational models of affordance (Zech et al, 2017). In this sense self-organizing attractors play an important role in the generation of useful behavior for the discovery of dynamic object affordances (Der & Martius, 2017).…”

“…important role in the generation of useful behavior for the discovery of dynamic object affordances (Der & Martius, 2017).…”

From Biological Synapses to “Intelligent” Robots

“…On the basis of mere feedback through the internal dynamics of an object, a robot is, like a human, able to relate to specific objects with a very specific sensorimotor pattern. When the object-specific dynamical patterns are combined with hand coordinates obtained from a camera, dedicated hand-eye coordination self-organizes spontaneously [152][153][154] without any higher-order cognitive control. Robots are currently not capable of any form of genuine cognition.…”

Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm

“…Let us now have a look at the main characteristics of the control approach, which is also discussed in [14]. This paper goes into more detail and present new experiments, see Tab.…”