Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.Comment: To appear at CVPR 201

The evolutionary origins of hierarchy

Hierarchical organization -- the recursive composition of sub-modules -- is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force--the cost of connections--promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.Comment: 32 page

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications

Posture control of a low-cost commercially available hexapod robot for uneven terrain locomotion

Legged robots hold the advantage on uneven and irregular terrain, where they exhibit superior mobility over other terrestrial, mobile robots. One of the fundamental ingredients in achieving this exceptional mobility on uneven terrain is posture control, also referred to as attitude control. Many approaches to posture control for multi-legged robots have been taken in the literature; however, the majority of this research has been conducted on custom designed platforms, with sophisticated hardware and, often, fully custom software. Commercially available robots hardly feature in research on uneven terrain locomotion of legged robots, despite the significant advantages they pose over custom designed robots, including drastically lower costs, reusability of parts, and reduced development time, giving them the serious potential to be employed as low-cost research and development platforms. Hence, the aim of this study was to design and implement a posture control system on a low-cost, commercially available hexapod robot – the PhantomX MK-II – overcoming the limitations presented by the lower cost hardware and open source software, while still achieving performance comparable to that exhibited by custom designed robots. For the initial controller development, only the case of the robot standing on all six legs was considered, without accounting for walking motion. This Standing Posture Controller made use of the Virtual Model Control (VMC) strategy, along with a simple foot force distribution rule and a direct force control method for each of the legs, the joints of which can only be position controlled (i.e. they do not have torque control capabilities). The Standing Posture Controller was experimentally tested on level and uneven terrain, as well as on a dynamic balance board. Ground truth measurements of the posture during testing exhibited satisfactory performance, which compared favourably to results of similar tests performed on custom designed platforms. Thereafter, the control system was modified for the more general case of walking. The Walking Posture Controller still made use of VMC for the high-level posture control, but the foot force distribution was expanded to also account for a tripod of ground contact legs during walking. Additionally, the foot force control structure was modified to achieve compliance control of the legs during the swing phase, while still providing direct force control during the stance phase, using the same overall control structure, with a simple switching strategy, all without the need for torque control or modification of the motion control system of the legs, resulting in a novel foot force control system for low-cost, legged robots. Experimental testing of the Walking Posture Controller, with ground truth measurements, revealed that it improved the robot’s posture response by a small amount when walking on flat terrain, while on an uneven terrain setup the maximum roll and pitch angle deviations were reduced by up to 28.6% and 28.1%, respectively, as compared to the uncompensated case. In addition to reducing the maximum deviations on uneven terrain, the overall posture response was significantly improved, resulting in a response much closer to that observed on flat terrain, throughout much of the uneven terrain locomotion. Comparing these results to those obtained in similar tests performed with more sophisticated, custom designed robots, it is evident that the Walking Posture Controller exhibits favourable performance, thus fulfilling the aim of this study.Dissertation (MEng)--University of Pretoria, 2018.Mechanical and Aeronautical EngineeringMEngUnrestricte