Stage-Wise Learning of Reaching Using Little Prior Knowledge

In some manipulation robotics environments, because of the difficulty of precisely modeling dynamics and computing features which describe well the variety of scene appearances, hand-programming a robot behavior is often intractable. Deep reinforcement learning methods partially alleviate this problem in that they can dispense with hand-crafted features for the state representation and do not need pre-computed dynamics. However, they often use prior information in the task definition in the form of shaping rewards which guide the robot toward goal state areas but require engineering or human supervision and can lead to sub-optimal behavior. In this work we consider a complex robot reaching task with a large range of initial object positions and initial arm positions and propose a new learning approach with minimal supervision. Inspired by developmental robotics, our method consists of a weakly-supervised stage-wise procedure of three tasks. First, the robot learns to fixate the object with a 2-camera system. Second, it learns hand-eye coordination by learning to fixate its end-effector. Third, using the knowledge acquired in the previous steps, it learns to reach the object at different positions and from a large set of initial robot joint angles. Experiments in a simulated environment show that our stage-wise framework yields similar reaching performances, compared with a supervised setting without using kinematic models, hand-crafted features, calibration parameters or supervised visual modules

Hierarchical representations for spatio-temporal visual attention: modeling and understanding

Mención Internacional en el título de doctorDentro del marco de la Inteligencia Artificial, la Visión Artificial es una disciplina científica que tiene como objetivo simular automaticamente las funciones del sistema visual humano, tratando de resolver tareas como la localización y el reconocimiento de objetos, la detección de eventos o el seguimiento de objetos....Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Salgado Álvarez de Sotomayor.- Secretario: Ascensión Gallardo Antolín.- Vocal: Jenny Benois Pinea

Real-Time Prediction and Decision Making in Connected and Automated Vehicles Under Cyber-Security and Safety Uncertainties

Our current transportation system is on the brink of transforming into a highly connected,automated, and intelligent system as a result of the rapid emergence of connected andautomated vehicles (CAVs). CAVs, with various levels of automation, are expected toincrease overall road safety, reduce travel time, improve comfort, improve fuel efficiency, anddecrease fatal accidents in the near future. CAVs use a combination of cameras, ultrasonicsensors, and radar to build a digital map of their surroundings and operate the vehicleaccordingly. As a result, there are numerous sources of information that can be manipulated,with malicious or non-malicious intent, which may result in dangerous situations. Althoughthe ever-increasing use of CAV technologies in vehicles are expected to have numerousadvantages, they can give rise to new challenges in terms of safety, security, and privacy.As evident by recent crash records and experiments successfully conducting cyber attacks onvehicles, the currently available autonomous systems lack the ability to fully handle novel,complex situations. Hence, the potential drawbacks of CAVs are not negligible and shouldnot be ignored. In this study, we investigate the real-time prediction and decision makingin CAVs under cyber-security and safety uncertainties

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Optometric extension program: 1991 bibliography of near lenses and vision training research

Optometric extension program: 1991 bibliography of near lenses and vision training researc

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 145

This bibliography lists 301 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1975

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method