Internal visuomotor models for cognitive simulation processes

Kaiser A. Internal visuomotor models for cognitive simulation processes. Bielefeld: Bielefeld University; 2014.Recent theories in cognitive science step back from the strict separation of perception, cognition, and the generation of behavior. Instead, cognition is viewed as a distributed process that relies on sensory, motor and affective states. In this notion, internal simulations -i.e. the mental reenactment of actions and their corresponding perceptual consequences - replace the application of logical rules on a set of abstract representations. These internal simulations are directly related to the physical body of an agent with its designated senses and motor repertoire. Correspondingly, the environment and the objects that reside therein are not viewed as a collection of symbols with abstract properties, but described in terms of their action possibilities, and thus as reciprocally coupled to the agent. In this thesis we will investigate a hypothetical computational model that enables an agent to infer information about specific objects based on internal sensorimotor simulations. This model will eventually enable the agent to reveal the behavioral meaning of objects. We claim that such a model would be more powerful than classical approaches that rely on the classification of objects based on visual features alone. However, the internal sensorimotor simulation needs to be driven by a number of modules that model certain aspects of the agents senses which is, especially for the visual sense, demanding in many aspects. The main part of this thesis will deal with the learning and modeling of sensorimotor patterns which represents an essential prerequisite for internal simulation. We present an efficient adaptive model for the prediction of optical flow patterns that occur during eye movements: This model enables the agent to transform its current view according to a covert motor command to virtually fixate a given point within its visual field. The model is further simplified based on a geometric analysis of the problem. This geometric model also serves as a solution to the problem of eye control. The resulting controller generates a kinematic motor command that moves the eye to a specific location within the visual field. We will investigate a neurally inspired extension of the eye control scheme that results in a higher accuracy of the controller. We will also address the problem of generating distal stimuli, i.e. views of the agent's gripper that are not present in its current view. The model we describe associates arm postures to pictorial views of the gripper. Finally, the problem of stereoptic depth perception is addressed. Here, we employ visual prediction in combination with an eye controller to generate virtually fixated views of objects in the left and right camera images. These virtually fixated views can be easily matched in order to establish correspondences. Furthermore, the motor information of the virtual fixation movement can be used to infer depth information

Peripersonal Space in the Humanoid Robot iCub

Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities. This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks. The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages. The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology

Building Brains for Bodies

We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We will build an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to "think'' by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012