Movement imitation mechanisms in robots and humans

Imitation mechanisms in artificial and biological agents are of great interest mainly for two reasons: from the engineering point of view, they allow the agent to efficiently utilise the knowledge of other agents in its social environment in order to quickly learn how to perform new tasks; from the scientific point of view, these mechanisms are in¬ triguing since they require the integration of information from the visual, memory, and motor systems. This thesis presents a dual-route architecture for movement imitation and considers its plausibility as a computational model of primate movement imitation mechanisms.The developed architecture consists of two routes, termed passive and active. The active route tightly couples behaviour perception and generation: in order to perceive a demonstrated behaviour, the motor behaviours already in the imitator's repertoire are utilised. While the demonstration is unfolding, these behaviours are executed on internal forward models, and predictions are generated with respect to what the next state of the demonstrator will be. Behaviours are reinforced based on the accuracy of these predictions. Imitation amounts to selecting the behaviour that performed best, and re-enacting that behaviour. If none of the existing behaviours performs adequately, control is passed to the passive route, which extracts the representative postures that describe the demonstrated behaviour, and imitates it by sequentially going through the extracted postures. Demonstrated behaviours imitated through the passive route form the basis for acquiring new behaviours, which are added to the repertoire available to the active route. A stereo vision robotic head, and a dynamically simulated 13 DoF articulated robot are utilised in order to implement this architecture, illustrate its behavioural characteristics, and investigate its capabilities and limitations. The experiments show the architecture being capable of imitating and learning a variety of head and arm movements, while they highlight its inability to perceive a behaviour that is in the imitator's repertoire, if the behaviour is demonstrated with execution parameters (for example, speed) unattainable by the imitator.This thesis also proposes this architecture as a computational model of primate move¬ ment imitation mechanisms. The behavioural characteristics of the architecture are compared with biological data available on monkey and human imitation mechanisms. The behaviour of the active route correlates favourably with brain activation data, both at the neuronal level (monkey's F5 'mirror neurons'), and at the systems level (human PET and MEP data that demonstrate activation of motor areas during ac¬ tion observation and imagination). The limitations of the architecture that surfaced during the computational experiments lead to testable predictions regarding the beha¬ viour of mirror neurons. The passive route is a computational implementation of an intermodal-matching mechanism, that has been hypothesised to underlie early infant movement imitation (the AIM hypothesis). Destroying the passive route leads to the architecture being unable to imitate any novel behaviours, but retaining its ability to imitate known ones. This characteristic correlates favourably with the symptoms dis¬ played by humans suffering from visuo-imitative apraxia. Finally, dealing with novel vs. known behaviours through separate routes correlates favourably with human brain activation (PET) data which show that the pattern of activation differs according to whether the observed action is meaningful or not to the observer

Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment

It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (“multiple timescales”). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems

Gaze control modelling and robotic implementation

Although we have the impression that we can process the entire visual field in a single fixation, in reality we would be unable to fully process the information outside of foveal vision if we were unable to move our eyes. Because of acuity limitations in the retina, eye movements are necessary for processing the details of the array. Our ability to discriminate fine detail drops off markedly outside of the fovea in the parafovea (extending out to about 5 degrees on either side of fixation) and in the periphery (everything beyond the parafovea). While we are reading or searching a visual array for a target or simply looking at a new scene, our eyes move every 200-350 ms. These eye movements serve to move the fovea (the high resolution part of the retina encompassing 2 degrees at the centre of the visual field) to an area of interest in order to process it in greater detail. During the actual eye movement (or saccade), vision is suppressed and new information is acquired only during the fixation (the period of time when the eyes remain relatively still). While it is true that we can move our attention independently of where the eyes are fixated, it does not seem to be the case in everyday viewing. The separation between attention and fixation is often attained in very simple tasks; however, in tasks like reading, visual search, and scene perception, covert attention and overt attention (the exact eye location) are tightly linked. Because eye movements are essentially motor movements, it takes time to plan and execute a saccade. In addition, the end-point is pre-selected before the beginning of the movement. There is considerable evidence that the nature of the task influences eye movements. Depending on the task, there is considerable variability both in terms of fixation durations and saccade lengths. It is possible to outline five separate movement systems that put the fovea on a target and keep it there. Each of these movement systems shares the same effector pathway—the three bilateral groups of oculomotor neurons in the brain stem. These five systems include three that keep the fovea on a visual target in the environment and two that stabilize the eye during head movement. Saccadic eye movements shift the fovea rapidly to a visual target in the periphery. Smooth pursuit movements keep the image of a moving target on the fovea. Vergence movements move the eyes in opposite directions so that the image is positioned on both foveae. Vestibulo-ocular movements hold images still on the retina during brief head movements and are driven by signals from the vestibular system. Optokinetic movements hold images during sustained head rotation and are driven by visual stimuli. All eye movements but vergence movements are conjugate: each eye moves the same amount in the same direction. Vergence movements are disconjugate: The eyes move in different directions and sometimes by different amounts. Finally, there are times that the eye must stay still in the orbit so that it can examine a stationary object. Thus, a sixth system, the fixation system, holds the eye still during intent gaze. This requires active suppression of eye movement. Vision is most accurate when the eyes are still. When we look at an object of interest a neural system of fixation actively prevents the eyes from moving. The fixation system is not as active when we are doing something that does not require vision, for example, mental arithmetic. Our eyes explore the world in a series of active fixations connected by saccades. The purpose of the saccade is to move the eyes as quickly as possible. Saccades are highly stereotyped; they have a standard waveform with a single smooth increase and decrease of eye velocity. Saccades are extremely fast, occurring within a fraction of a second, at speeds up to 900°/s. Only the distance of the target from the fovea determines the velocity of a saccadic eye movement. We can change the amplitude and direction of our saccades voluntarily but we cannot change their velocities. Ordinarily there is no time for visual feedback to modify the course of the saccade; corrections to the direction of movement are made in successive saccades. Only fatigue, drugs, or pathological states can slow saccades. Accurate saccades can be made not only to visual targets but also to sounds, tactile stimuli, memories of locations in space, and even verbal commands (“look left”). The smooth pursuit system keeps the image of a moving target on the fovea by calculating how fast the target is moving and moving the eyes accordingly. The system requires a moving stimulus in order to calculate the proper eye velocity. Thus, a verbal command or an imagined stimulus cannot produce smooth pursuit. Smooth pursuit movements have a maximum velocity of about 100°/s, much slower than saccades. The saccadic and smooth pursuit systems have very different central control systems. A coherent integration of these different eye movements, together with the other movements, essentially corresponds to a gating-like effect on the brain areas controlled. The gaze control can be seen in a system that decides which action should be enabled and which should be inhibited and in another that improves the action performance when it is executed. It follows that the underlying guiding principle of the gaze control is the kind of stimuli that are presented to the system, by linking therefore the task that is going to be executed. This thesis aims at validating the strong relation between actions and gaze. In the first part a gaze controller has been studied and implemented in a robotic platform in order to understand the specific features of prediction and learning showed by the biological system. The eye movements integration opens the problem of the best action that should be selected when a new stimuli is presented. The action selection problem is solved by the basal ganglia brain structures that react to the different salience values of the environment. In the second part of this work the gaze behaviour has been studied during a locomotion task. The final objective is to show how the different tasks, such as the locomotion task, imply the salience values that drives the gaze

Social Cognition for Human-Robot Symbiosis—Challenges and Building Blocks

The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a “positronic” replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud. In this article, we review the challenges that must be met in the design of a set of interacting computational processes as building blocks of a cognitive architecture that may give symbiotic capabilities to collaborative robots of the next decades: (1) an animated body-schema; (2) an imitation machinery; (3) a motor intentions machinery; (4) a set of physical interaction mechanisms; and (5) a shared memory system for incremental symbiotic development. We would like to stress that our approach is totally un-hierarchical: the five building blocks of the shared cognitive architecture are fully bi-directionally connected. For example, imitation and intentional processes require the “services” of the animated body schema which, on the other hand, can run its simulations if appropriately prompted by imitation and/or intention, with or without physical interaction. Successful experiences can leave a trace in the shared memory system and chunks of memory fragment may compete to participate to novel cooperative actions. And so on and so forth. At the heart of the system is lifelong training and learning but, different from the conventional learning paradigms in neural networks, where learning is somehow passively imposed by an external agent, in symbiotic robots there is an element of free choice of what is worth learning, driven by the interaction between the robot and the human partner. The proposed set of building blocks is certainly a rough approximation of what is needed by symbiotic robots but we believe it is a useful starting point for building a computational framework

Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network

© 2018 The Authors Recent advances in behavioural and computational neuroscience, cognitive robotics, and in the hardware implementation of large-scale neural networks, provide the opportunity for an accelerated understanding of brain functions and for the design of interactive robotic systems based on brain-inspired control systems. This is especially the case in the domain of action and language learning, given the significant scientific and technological developments in this field. In this work we describe how a neuroanatomically grounded spiking neural network for visual attention has been extended with a word learning capability and integrated with the iCub humanoid robot to demonstrate attention-led object naming. Experiments were carried out with both a simulated and a real iCub robot platform with successful results. The iCub robot is capable of associating a label to an object with a ‘preferred’ orientation when visual and word stimuli are presented concurrently in the scene, as well as attending to said object, thus naming it. After learning is complete, the name of the object can be recalled successfully when only the visual input is present, even when the object has been moved from its original position or when other objects are present as distractors

Perception and action without 3D coordinate frames

Neuroscientists commonly assume that the brain generates representations of a scene in various non-retinotopic 3D coordinate frames, for example in 'egocentric' and 'allocentric' frames. Although neurons in early visual cortex might be described as representing a scene in an eye-centred frame, using 2 dimensions of visual direction and one of binocular disparity, there is no convincing evidence of similarly organized cortical areas using non-retinotopic 3D coordinate frames nor of any systematic transfer of information from one frame to another. We propose that perception and action in a 3D world could be achieved without generating ego- or allocentric 3D coordinate frames. Instead, we suggest that the fundamental operation the brain carries out is to compare a long state vector with a matrix of weights (essentially, a long look-up table) to choose an output (often, but not necessarily, a motor output). The processes involved in perception of a 3D scene and action within it depend, we suggest, on successive iterations of this basic operation. Advantages of this proposal include the fact that it relies on computationally well-defined operations corresponding to well-established neural processes. Also, we argue that from a philosophical perspective it is at least as plausible as theories postulating 3D coordinate frames. Finally, we suggest a variety of experiments that would falsify our claim

Reference Frames in Human Sensory, Motor, and Cognitive Processing

Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus on motor behavior, more specifically on goal-directed reaching. We develop a model that explains how egocentric perceptual and motor reference-frames can be coordinated through exocentric reference-frames. Finally, in a third project, we study how the cognitive system can store and recognize objects by using sensorimotor schema that allows mental rotation within an exocentric reference-frame