Motor learning through the combination of primitives

Abstract

In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a speci¢c force ¢eld and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding ¢elds. We regard these force ¢elds as computational primitives that are used by the CNS for generating a rich grammar of motor behaviours.

Combining modules for movement

“…Bizzi et al (1991), Mussa-Ivaldi et al (1994), and more recently, Lemay et al (2001) have shown that the simultaneous stimulation of two sites, each generating a different force field, results in the vector sum of the two fields in most instances. When the pattern of forces recorded at the ankle following co-stimulation were compared with those computed by summation of the two individual fields, Mussa-Ivaldi et al (1994) found that the "co-stimulation fields" and the "summation fields" were equivalent in more than 87% of cases. Similar results have been obtained by Tresch and Bizzi, (1999) by stimulating the spinal cord of the rat.…”

Internal models in sensorimotor integration: perspectives from adaptive control theory

“…Although the internal model and impedance control hypotheses were initially thought to be contradictory to one another (but see (Mussa-Ivaldi and Bizzi 2000)), recent studies suggest that they could co-exist during motor learning (Burdet, Osu. et al 2001;Franklin, Osu et al 2003).…”

Human Motion Synthesis by Motion Manifold Learning and Motion Primitive Segmentation

“…Then, We learn nonlinear mapping between the low dimensional manifold representation and the original high dimensional motion. The low dimensional manifold representation is motivated by force fields in the biological study of human motion [10]. The motion primitives that we are interested in are relevant to the intrinsic body configuration and irrelevant to the position and orientation of the body.…”

“…Biological study shows that complicated human motions are controlled by linear combination of computational motion primitives called force fields [10]. We learn a generative model with a low dimensional motion manifold representation similar to force fields of motion primitives.…”