Abstract
In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the “degrees of freedom (DoFs) problem,” the common core of production, observation, reasoning, and learning of “actions.” OCT, directly derived from engineering design techniques of control systems quantifies task goals as “cost functions” and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative “softer” approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that “animates” the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints “at runtime,” hence solving the “DoFs problem” without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of “potential actions.” In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures.
Highlights
Learning to attain specific “body postures” that are required by the task
In Section “Motor Skill Learning and PMP,” we have shown how this information can be learnt through an “action–perception” loop and represented in a sub-symbolic manner using standard neural networks
This can be achieved by learning the right balance of “admittance” in the intrinsic space of the associated PMP network
Summary
Learning to attain specific “body postures” that are required by the task. Attaining the right body pose in some tasks may be obligatory but most often simplifies the execution of any motor skill. Preliminary results have been obtained in the scenario of whole body synergy formation (Morasso et al, 2010; Zenzeri, 2010) where the task was to learn the right balance of admittance values in the whole body PMP network in order to reproduce the final body pose achieved by the teacher (recorded using a motion capture device). Even though it applies for this specific case, a more comprehensive and general understanding needs to be achieved through experiments in the future. While the link between perception of movement and task-specific regulation of “stiffness” and “timing” in the extrinsic space (VTGS) has been already addressed in detail, further research needs to be conducted to understand the link between perception of movement and swift “task–specific” regulation of admittance in the intrinsic space, in order to obtain specific body postures while performing these movements
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