Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling

In this dissertation, we present an abstract model of infant reaching that is neurally-plausible. This model is grounded in embodied artificial intelligence, which emphasizes the importance of the sensorimotor interaction of an agent and the world. It includes both learning sensorimotor correlations through motor babbling and also arm motion planning using spreading activation. We introduce a mechanism called bundle formation as a way to generalize motions during the motor babbling stage. We then offer a neural model for the abstract model, which is composed of three layers of neural maps with parallel structures representing the same sensorimotor space. The motor babbling period shapes the structure of the three neural maps as well as the connections within and between them; these connections encode trajectory bundles in the neural maps. We then investigate an implementation of the neural model using a reaching task on a humanoid robot. Through a set of experiments, we were able to find the best way to implement different components of this model such as motor babbling, neural representation of sensorimotor space, dimension reduction, path planning, and path execution. After the proper implementation had been found, we conducted another set of experiments to analyze the model and evaluate the planned motions. We evaluated unseen reaching motions using jerk, end effector error, and overshooting. In these experiments, we studied the effect of different dimensionalities of the reduced sensorimotor space, different bundle widths, and different bundle structures on the quality of arm motions. We hypothesized a larger bundle width would allow the model to generalize better. The results confirmed that the larger bundles lead to a smaller error of end-effector position for testing targets. An experiment with the resolution of neural maps showed that a neural map with a coarse resolution produces less smooth motions compared to a neural map with a fine resolution. We also compared the unseen reaching motions under different dimensionalities of the reduced sensorimotor space. The results showed that a smaller dimension leads to less smooth and accurate movements

Learning Sensorimotor Abstractions

Projecte final de carrera fet en col.laboració amb Aalto University. School of Science and Technology. Faculty of Information and Natural SciencesIn order to interact with real environments, performing daily tasks, autonomous agents (as machines or robots) cannot be hard-coded. Given all the possible scenarios and, in each scenario, all the possible variations, it is impossible to take into account every single situation that the autonomous agent may encounter. Humans are able to interact with the changing world using as a guidance the sensory input perceived. Thus, autonomous agents need to be able to adapt to a changing environment. This work proposes a biologically inspired solution that allows the agent to learn representations and skills autonomously that prepare the agent for future learning tasks. The biologically inspired solution proposed here, called a cognitive architecture, follows the hierarchical architecture found in the cerebral cortex. This model permits the autonomous agent to extract useful information from the sensory input data it receives. The information is coded in abstractions, which are invariant features found within the input patterns. The cognitive architecture uses slowness as a principle for extracting features. In principle, unsupervised learning algorithms based on slowness try to find relevant and slowly changing data. This information could be useful for self evaluation. The agent tries to learn how to manipulate the sensory abstractions, by linking those to the motor ones. This allows the robot to find the mapping between the motor actions it is taking and the changes it is able to produce in the surrounding environment. Using the cognitive architecture, an example will be implemented. An agent, who knows nothing about the environment it is placed on, will be able to learn how to move towards different places in space in an efficient (not random) way. Starting from random movements and capturing the sensory input data, it is able to learn concepts such as place and distance, which permits it to learn how to move towards a target efficiently

Bootstrapping Relational Affordances of Object Pairs using Transfer

This work was supported in part by the U.K. EPSRC DTG EP/J5000343/1 at Aberdeen, and in part by the EU Cognitive Systems Project XPERIENCE at SDU under Grant FP7-ICT-270273.Peer reviewedPostprin

What is Robotics: Why Do We Need It and How Can We Get It?

Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots. For more information: Kod*la

A MULTIPLE REPRESENTATIONS MODEL OF THE HUMAN MIRROR NEURON SYSTEM FOR LEARNED ACTION IMITATION

The human mirror neuron system (MNS) is a fundamental sensorimotor system that plays a critical role in action observation and imitation. Despite a large body of experimental and theoretical MNS studies, the visuospatial transformation between the observed and the imitated actions has received very limited attention. Therefore, this work proposes a neurobiologically plausible MNS model, which examines the dynamics between the fronto-parietal mirror system and the parietal visuospatial transformation system during action observation and imitation. The fronto-parietal network is composed of the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL), which are postulated to generate the neural commands and the predictions for its sensorimotor consequences, respectively. The parietal regions identified as the superior parietal lobule (SPL) and the intraparietal sulcus (IPS) are postulated to encode the visuospatial transformation for enabling view-independent representations of the observed action. The middle temporal region is postulated to provide the view-dependent representations such as direction and velocity of the observed action. In this study, the SPL/IPS, IFG, and IPL are modeled with artificial neural networks to simulate the neural mechanisms underlying action imitation. The results reveal that this neural model can replicate relevant behavioral and neurophysiological findings obtained from previous action imitation studies. Specifically, the imitator can replicate the observed actions independently of the spatial relationships with the demonstrator while generating similar synthetic functional magnetic resonance imaging blood oxygenation level-dependent responses in the IFG for both action observation and execution. Moreover, the SPL/IPS can provide view-independent visual representations through mental transformation for which the response time monotonically increases as the rotation angle augments. Furthermore, the simulated neural activities reveal the emergence of both view-independent and view-dependent neural populations in the IFG. As a whole, this work suggests computational mechanisms by which visuospatial transformation processes would subserve the MNS for action observation and imitation independently of the differences in anthropometry, distance, and viewpoint between the demonstrator and the imitator

Kinematic strategies in newly walking toddlers stepping over different support surfaces

In adults, locomotor movements are accommodated to various support surface conditions by means of specific anticipatory locomotor adjustments and changes in the intersegmental coordination. Here we studied the kinematic strategies of toddlers at the onset of independent walking when negotiating various support surface conditions: stepping over an obstacle, walking on an inclined surface, and on a staircase. Generally, toddlers could perform these tasks only when supported by the arm. They exhibited strategies very different from those of the adults. Although adults maintained walking speed roughly constant, toddlers markedly accelerated when walking downhill or downstairs and decelerated when walking uphill or upstairs. Their coordination pattern of thigh-shank-foot elevation angles exhibited greater inter-trial variability than that in adults, but it did not undergo the systematic change as a function of task that was present in adults. Thus the intersegmental covariance plane rotated across tasks in adults, whereas its orientation remained roughly constant in toddlers. In contrast with the adults, the toddlers often tended to place the foot onto the obstacle or across the edges of the stairs. We interpret such foot placements as part of a haptic exploratory repertoire and we argue that the maintenance of a roughly constant planar covariance--irrespective of the surface inclination and height--may be functional to the exploratory behavior. The latter notion is consistent with the hypothesis proposed decades ago by Bernstein that, when humans start to learn a skill, they may restrict the number of degrees of freedom to reduce the size of the search space and simplify the coordination

Intelligent Agent Architectures: Reactive Planning Testbed

An Integrated Agent Architecture (IAA) is a framework or paradigm for constructing intelligent agents. Intelligent agents are collections of sensors, computers, and effectors that interact with their environments in real time in goal-directed ways. Because of the complexity involved in designing intelligent agents, it has been found useful to approach the construction of agents with some organizing principle, theory, or paradigm that gives shape to the agent's components and structures their relationships. Given the wide variety of approaches being taken in the field, the question naturally arises: Is there a way to compare and evaluate these approaches? The purpose of the present work is to develop common benchmark tasks and evaluation metrics to which intelligent agents, including complex robotic agents, constructed using various architectural approaches can be subjected

Autonomous Behaviors With A Legged Robot

Over the last ten years, technological advancements in sensory, motor, and computational capabilities have made it a real possibility for a legged robotic platform to traverse a diverse set of terrains and execute a variety of tasks on its own, with little to no outside intervention. However, there are still several technical challenges to be addressed in order to reach complete autonomy, where such a platform operates as an independent entity that communicates and cooperates with other intelligent systems, including humans. A central limitation for reaching this ultimate goal is modeling the world in which the robot is operating, the tasks it needs to execute, the sensors it is equipped with, and its level of mobility, all in a unified setting. This thesis presents a simple approach resulting in control strategies that are backed by a suite of formal correctness guarantees. We showcase the virtues of this approach via implementation of two behaviors on a legged mobile platform, autonomous natural terrain ascent and indoor multi-flight stairwell ascent, where we report on an extensive set of experiments demonstrating their empirical success. Lastly, we explore how to deal with violations to these models, specifically the robot\u27s environment, where we present two possible extensions with potential performance improvements under such conditions