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Goal Babbling: a New Concept for Early Sensorimotor Exploration

The human body possesses more than 600 skeletal muscles [1]. Performing purposeful actions to achieve some behavioral goal requires a high degree of coordination of these many degrees of freedom. Yet, human infants are born without the most basic coordination skills like reaching for an object [2], which poses the learning of sensorimotor coordination as a fundamental problem in human development. Understanding this ability to learn, and utilizing it for modern robotics systems is one of the major goals of the research fields of cognitive [3] and developmental robotics [4], [5]. We investigate the learning of reaching skills as an exemplary coordination skill. The problem of reaching is to find motor commands (e.g. joint angles of a robot arm) that move the hand, or the robot’s end-effector towards some desired position in space. Thereby motor commands q and outcomes x are connected by a causal relation which is denoted as the forward function f(q) = x. Learning needs to invert this relation in order achieve some desired outcome x∗. This problem setup is not only illustrative, but very prototypical for other coordination problems: it asks the very general question of how to achieve some behavioral goals by means of actions. The skill of reaching itself is also fundamental for both robots and humans, since the positioning in space is necessary for any use of the robot’s gripper or the human’s hand. Successful reaching skills can be well understood with the notion of internal models [6], [7], whereas forward models predict the outcome of an action and inverse models suggest actions in order to achieve a desired outcome. The bootstrapping of internal models without explicit prior-knowledge requires experience that has to be generated by exploration. Machine learning approaches thereby traditionally rely on an exhaustive exploration of all possible motor commands, frequently generated by means of an entire random procedure, which is referred to as “motor babbling” [8], [9]. After the data generation phase, learning and coordination can be phrased in a variety of ways [10], [11], [12]. Yet, exhaustive exploration can not be achieved on high-dimensional motor systems such as the human body, modern humanoid robots, or biomimetic robots like elephant trunks. The sheer number of combinations of commands for different actuators is too large to be explored in the lifetime of any learning agent. Understanding human motor development, as well as the successful application of future robotic systems like the Bionic Handling Assistant (see Fig. 3), demands for concepts and methods that succeed in