A lesson from robotics: Modeling infants as autonomous agents

While computational models are playing an increasingly important role in developmental psychology, at least one lesson from robotics is still being learned: modeling epigenetic processes often requires simulating an embodied, autonomous organism. This paper first contrasts prevailing models of infant cognition with an agent-based approach. A series of infant studies by Baillargeon (1986; Baillargeon & DeVos, 1991) is described, and an eye-movement model is then used to simulate infants' visual activity in this study. I conclude by describing three behavioral predictions of the eyemovement model, and discussing the implications of this work for infant cognition research

Visual Reference and Iconic Content

Evidence from cognitive science supports the claim that humans and other animals see the world as divided into objects. Although this claim is widely accepted, it remains unclear whether the mechanisms of visual reference have representational content or are directly instantiated in the functional architecture. I put forward a version of the former approach that construes object files as icons for objects. This view is consistent with the evidence that motivates the architectural account, can respond to the key arguments against representational accounts, and has explanatory advantages. I draw general lessons for the philosophy of perception and the naturalization of intentionality

Naive Realism for Unconscious Perceptions

Unconscious perceptions (i.e., person-level perceptions that lack phenomenal character) have recently become a focal point in the debate for and against naive realism. In this paper I defend the naive realist side. More specifically, I use an idea of Martin’s to develop a new version of naive realism - neuro-computational naive realism. I argue that neuro-computational naive realism offers a uniform treatment of both conscious and unconscious perceptions. I also argue that it accommodates the possibility of phenomenally different conscious perceptions of the same items, and that it can answer a further challenge to naive realism raised by Berger and Nanay

Deep Drone Racing: From Simulation to Reality with Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854

Applications of computer-graphics animation for motion-perception research

The advantages and limitations of using computer animated stimuli in studying motion perception are presented and discussed. Most current programs of motion perception research could not be pursued without the use of computer graphics animation. Computer generated displays afford latitudes of freedom and control that are almost impossible to attain through conventional methods. There are, however, limitations to this presentational medium. At present, computer generated displays present simplified approximations of the dynamics in natural events. Very little is known about how the differences between natural events and computer simulations influence perceptual processing. In practice, the differences are assumed to be irrelevant to the questions under study, and that findings with computer generated stimuli will generalize to natural events

Helicopter human factors research

Helicopter flight is among the most demanding of all human-machine integrations. The inherent manual control complexities of rotorcraft are made even more challenging by the small margin for error created in certain operations, such as nap-of-the-Earth (NOE) flight, by the proximity of the terrain. Accident data recount numerous examples of unintended conflict between helicopters and terrain and attest to the perceptual and control difficulties associated with low altitude flight tasks. Ames Research Center, in cooperation with the U.S. Army Aeroflightdynamics Directorate, has initiated an ambitious research program aimed at increasing safety margins for both civilian and military rotorcraft operations. The program is broad, fundamental, and focused on the development of scientific understandings and technological countermeasures. Research being conducted in several areas is reviewed: workload assessment, prediction, and measure validation; development of advanced displays and effective pilot/automation interfaces; identification of visual cues necessary for low-level, low-visibility flight and modeling of visual flight-path control; and pilot training