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N I V ER S TH Y IT E U R G H O F E D I U N B Division of Informatics, University of Edinburgh Institute of Perception, Action and Behaviour An Imitation Mechanism Inspired from Neurophysiology by George Maistros, Gillian Hayes Informatics Research Report EDI-INF-RR-0031 Division of Informatics http://www.informatics.ed.ac.uk/ August 2000 An imitation mechanism inspired from neurophysiology George Maistros Gillian Hayes Institute of Perception, Action, and Behaviour Division of Informatics University of Edinburgh, 5 Forrest Hill, Edinburgh, EH1 2QL, UK {georgem, gmh}@dai.ed.ac.uk Abstract found to have both visual and motor properties (i.e. they respond to both visual and motor stimuli). Imitation is employed with ease by all primates, but is rarely found in artificial agents. Recent monkey brain activation data illustrate interesting neural characteristics related to imitation. Inspired from these data and based on Arbib’s Schema Theory, we are implementing an architecture for imitative behaviours in artificial agents. 1 Motivation Imitation plays a very important role in biological agents. Primates can easily acquire useful information by imitating the behaviour of conspecifics. Imitation could also be very important to artificial agents. However, integrating sensory information with motor systems can be both very challenging and very hard. Neurophysiology projects new insights on imitation mechanisms. Experiments on non-human primates demonstrate neural characteristics like a ‘vocabulary’ of motor actions and action recognition (Rizzolatti and Fadiga, 1998). Jeannerod et al. (1995) and Demiris and Hayes (1998) have demonstrated how such insights may be introduced in the framework of imitation systems. In the light of the following neurophysiological results, we hope to develop an architecture with which we can demonstrate imitative behaviour in artificial agents. 2 Figure 1: Lateral view of macaque monkey cerebral cortex. Area F5 receives its main cortical input from AIP and projects its output onto the motor area F1. Abbreviations; Ps: principal sulcus; AIs: inferior arcuate sulcus; IPs: intraparietal sulcus; Cs: central sulcus. Figure adapted from Jeannerod et al. (1995), Figure 3. 2.1 Pre-motor area F5 di Pellegrino et al. (1992), Rizzolatti et al. (1996), and Gallese et al. (1996) experimented on the visual and motor properties of F5 neurons. They performed single neuron studies recording the activity of individual neurons at the presentation of various visual and motor stimuli. These recordings classified the F5 neurons into canonical neurons and mirror neurons. Most canonical neurons lie near the bank of the arcuate sulcus, while most mirror neurons near the cortical convexity. Both the canonical and the mirror neurons discharge when the monkey actively performs hand or mouth movements — motor stimuli. However, ca- Neurophysiological findings Neurophysiological research focusing on the activation of neurons in macaque monkeys’ brains found a very important class of neurons in the rostral part of inferior area 6 (pre-motor area F5, see Figure 1 and Rizzolatti et al. (1988)). These neurons were 1 tical input (see Figure 1), has similar visual and motor properties to F5 (Jeannerod et al., 1995). AIP is also involved in much of the feature extraction of the visual input (i.e. colour, lines, edges, faces, etc.) In fact, AIP forms strong reciprocal connections to F5 and it is proposed that AIP triggers appropriate F5 populations of neurons depending on the visual input (Rizzolatti et al., 2000). This is an interesting consideration which is discussed further in the next section. nonical neurons respond to different types of visual stimuli than mirror neurons. While canonical neurons discharge at the mere sight of an object, mirror neurons are triggered at the sight of hand or mouth interactions with objects. This uncovers the presence of an interesting visuomotor coupling mechanism. In particular, the discharge of F5 mirror neurons has been thoroughly explored and found highly consistent. The only goal-directed actions that trigger them are grasping, manipulating, and placing. Similarly, the only effective agents of those interactions are the hand and the mouth of the monkey or the experimenter. However, repeated observation of tool usage (e.g. pliers) has been informally reported to increase the corresponding activation (Rizzolatti and Arbib, 1998). The interacting objects themselves were found to be insignificant: the monkeys respond equally strongly to food as they do to solids, albeit they seem to fast lose interest in the latter. Rizzolatti et al. (1999) have also studied the temporal relation between the neural discharge and the interaction that triggered it. This showed behaviours like neurons discharging for the whole of the interaction, during early or late preshaping, and discharges starting shortly after contact with the object. Further studies classified the mirror neurons with respect to their visuomotor congruence. About 30% of them are strictly congruent, about 60% of them are broadly congruent, and nearly 8% of them are non-congruent. In strict congruence there is a correspondence between the observed and executed action both in terms of general action (e.g. grasp) and the type of action (e.g. precision grip). In broad congruence there is a loose correspondence between the observed and executed actions. For example, the neurons discharge when observing different kinds of grips and when executing a single kind of grip. In non-congruent neurons the visual stimulus bears no relation to the motor stimulus. Most of our interest lies in the understanding of the functional role of area F5 and especially in its implications for the development and demonstration of imitative mechanisms on robots. 3 Possible functional roles of area F5 The above neurophysiological findings suggest a strong coupling between observed actions (or objects) and the motor representation of these actions (or potential actions on these objects). A number of functional roles have been proposed mostly involving action understanding (di Pellegrino et al., 1992), action recognition (Rizzolatti et al., 1996), or a ‘vocabulary’ of motor actions (Rizzolatti and Fadiga, 1998). Most of these roles, however, are overlapping; they suggest a pre-motor system which possesses a vocabulary of motor actions. It is believed that this vocabulary serves a dual role. First, observed actions are mapped onto it (recognition and understanding) and second, actions are generated from it (behaviour or imitation). It is also proposed that mirror neurons offer the most obvious mapping between recognition and imitation — mirror neurons recognising an action can also generate that action. Another less obvious system that plays an equally important role lies outside the pre-motor cortex and is in AIP. Rizzolatti et al. (2000) suggest that AIP processes the visual input and extracts features like edges, faces, finger configurations, etc — features that are then used to selectively activate the F5 neurons that are directly relevant to the observed scene. 4 Possible architecture Our scope is to develop an architecture that both reflects the properties of F5 and serves its function. 2.2 Other areas The Schema Theory, originally proposed by Arbib The investigation of other areas neighbouring F5 of- (Arbib and Cobas, 1990), is specifically designed to fer even deeper comprehension of the function of the bridge the gap between brain theory and cognitive pre-motor system. The anterior intraparietal area science. Jeannerod et al. (1995) used the Schema (AIP), an area from which F5 receives its main cor- Theory for the simulation of brain functions similar 2 to the one of F5. The aim here is to apply the Schema Theory onto the above properties and roles. Arbib’s Schema Theory analyses brain functions assuming no localisation of neurons. This is achieved with an assembly of schemas. A schema is an active entity involved with either perception (perceptual schema) or motor control (motor schema). A perceptual schema deals with perceptual structures and their encoding. A motor schema deals with motor commands and their control. Schemas are combined to form a network which controls the passing of parameters from perceptual to motor schemas. Finally, the notion of a schema is recursive (i.e. a schema may be a network of other schemas). The concept of a schema adequately accommodates the main properties of F5 neurons: the visual properties correspond to perceptual schemas and the motor ones to motor schemas. Neurophysiology can be further reflected by constraining the way schemas are created and combined. Temporal studies suggest that neural behaviours are temporally segmented. Hence, each perceptual schema should only represent parts of observed actions. Similarly, each motor schema should only govern parts of actions. The visuomotor classification suggests different degrees of congruence. Strict congruence is expressed with each perceptual schema connected to exactly one motor schema. Broad congruence is expressed with few perceptual schemas connected to exactly one motor schema. Non-congruence, although less interesting, is expressed with perceptual schemas connected to a motor schema, such that the scene represented bears no relation to the action the motor schema controls. The selectivity of F5 neurons with respect to the agent of the interaction (i.e. hand or mouth) and the interaction itself (i.e. grasp, manipulation, etc.) is reflected by limiting the structures that perceptual schemas recognise. The non-selectivity towards the object is similar: the structures recognised should be independent of the nature of the object. The function of the F5 area is expressed when a vocabulary of schemas is formed. Such a vocabulary may use motor schemas to generate actions, perceptual schemas to recognise them, or both for imitation. Learning may take place by adjusting the actions that perceptual schemas recognise and motor schemas control. Finally, the function of the AIP area is expressed in the preprocessing of the visual input, such that only relevant perceptual schemas are active each time. We are currently implementing a schema network reflecting the aspects described above. This network would then be used by an artificial agent (imitator) aiming to recognise and imitate the actions of another (demonstrator). The demonstrator’s actions will primarily involve hand grasping only. The imitator should recognise and differentiate the observed grip among only a few others, and then reproduce it. This scenario, however minimal, requires most, if not all, of the features of this schema network. 5 Conclusion From a biological point of view, the F5 system facilitates a subject to recognise and understand the actions of others. From a computational point of view, this architecture facilitates efficient action recognition and imitation. The motor vocabulary by itself decreases the amount of variables one needs to control (for action generation) or instantiate (for action recognition). Furthermore, the function of the AIP area guides imitation and possibly learning, hence reducing the amount of complexity even further. The Schema Theory permits us to apply the neurophysiological constraints on the implementation of this architecture and finally test it on appropriate demonstrator-imitator scenarios. References Arbib, M. A. and Cobas, A. (1990). Schemas for prey-catching in frog and toad. From animals to animats (SAB), 1:142–151. Demiris, J. and Hayes, G. M. (1998). Active imitation. Dai research paper; no.936, Dept Artificial Intelligence, University of Edinburgh. di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., and Rizzolatti, G. (1992). Understanding motor events: a neurophysiological study. Experimental Brain Research, 91(1):176–180. Gallese, V., Fadiga, L., Fogassi, L., and Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119:593–609. Jeannerod, M., Arbib, M. A., and Rizzolatti, G. (1995). Grasping objects: the cortical mechanisms of visuomotor transformation. Trends In Neurosciences, 18(7):314–320. 3 Rizzolatti, G. and Arbib, M. A. (1998). Language withing our grasp. Trends In Neurosciences, 21(5):188–194. Rizzolatti, G., Carmada, R., Fogassi, L., Gentilucci, M., Luppino, G., and Matelli, M. (1988). Functional organization of inferior area 6 in macaque monkey. 2. Area F5 and the control of distal movements. Experimental Brain Research, 71(3):491–507. Rizzolatti, G. and Fadiga, L. (1998). Grasping objects and grasping action meanings: the dual role of monkey rostroventral premotor cortex (area F5). Novartis Foundation Symposium, 218:81– 103. In book: Sensory Guidance of Movement. Rizzolatti, G., Fadiga, L., Fogassi, L., and Gallese, V. (1999). Resonance behaviors and mirror neurons. Archives Italiennes de Biologie, 137:85–100. Rizzolatti, G., Fadiga, L., Gallese, V., and Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3(2):131–141. Rizzolatti, G., Fogassi, L., and Gallese, V. (2000). Cortical mechanisms subserving object grasping and action recognition: A new view on the cortical motor functions. In Gazzaniga, M., editor, The New Cognitive Neurosciences, pages 539– 552. Cambridge, MA: MIT Press. 4 An Imitation Mechanism Inspired from Neurophysiology George Maistros, Gillian Hayes Informatics Research Report EDI-INF-RR-0031 DIVISION of INFORMATICS Institute of Perception, Action and Behaviour August 2000 Abstract : Imitation is employed with ease by all primates, but is rarely found in artificial agents. Recent monkey brain activation data illustrate interesting neural characteristics related to imitation. Inspired from these data and based on Arbib’s Schema Theory, we are implementing an architecture for imitative behaviours in artificial agents. Keywords : imitation, robotic learning, mirror neurons, visuomotor coupling, macaque monkeys, area F5, inferior area 6, schema theory, affordances Copyright c 2000 by The University of Edinburgh. All Rights Reserved The authors and the University of Edinburgh retain the right to reproduce and publish this paper for non-commercial purposes. Permission is granted for this report to be reproduced by others for non-commercial purposes as long as this copyright notice is reprinted in full in any reproduction. Applications to make other use of the material should be addressed in the first instance to Copyright Permissions, Division of Informatics, The University of Edinburgh, 80 South Bridge, Edinburgh EH1 1HN, Scotland.