Anticipatory Mechanisms of Human Sensory-Motor Coordiantion Inspire Control of Adaptive Robots: a Brief Review

Abstract

Sensory-motor coordination involves the study of how organisms make accurate goaldirected movements based on perceived sensory information. There are two problems associated to this process: sensory feedback is noisy and delayed, which can make movements inaccurate and unstable, and the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. Nevertheless, we can observe everyday our ability to perform accurate movements, which is due to a nervous system that adapts to those existing limitations and continuously compensates for them. How does the nervous system do it? By means of anticipating the sensory consequences of motor commands. The idea that anticipatory mechanisms guide human behaviour, i.e., that predictions about future states directly influence current behavioural decision making, has been increasingly appreciated over the last decades. Various disciplines have explicitly recognized anticipations. In cognitive psychology, the ideo-motor principle states that an action is initiated by the anticipation of its effects, and before this advanced action mechanism can be used, a learning phase has to take place, advising the actor about several actions and their specific effects (Stock and Stock, 2004). In biorobotics, anticipation plays a major role in the coordination and performance of adaptive behaviour (Butz et al., 2002), being interested on designing artificial animals (animats) able to adapt to environmental changes efficiently by learning and drawing inferences. What are the bases of human anticipation mechanisms? Internal models of the body and the world. Internal models can be classified into (Miall & Wolpert, 1996): a. forward models, which are predictive models that capture the causal relationship between actions and outcome, translating the current system state and the current motor commands (efference copy) into predictions of the future system state, and b. inverse models, which generate from inputs about the system state and state transitions, an output representing the causal events that produced that state. Forward models are further divided into (Miall & Wolpert, 1996): i. forward dynamic models, estimating future system states after current motor commands,