Author Archives: Juan-antonio Fernández-madrigal

On how psychologists realize that the brain, after all, may be creating symbols (concepts), like deep neural networks show

Jeffrey S. Bowers, Parallel Distributed Processing Theory in the Age of Deep Networks, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 950-961, DOI: 10.1016/j.tics.2017.09.013.

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory.

Towards taking into account the complexity of finding the best option in decision-making systems

Peter Bossaerts, Carsten Murawski, Computational Complexity and Human Decision-Making, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 917-929, DOI: 10.1016/j.tics.2017.09.005.

The rationality principle postulates that decision-makers always choose the best action available to them. It underlies most modern theories of decision-making. The principle does not take into account the difficulty of finding the best option. Here, we propose that computational complexity theory (CCT) provides a framework for defining and quantifying the difficulty of decisions. We review evidence showing that human decision-making is affected by computational complexity. Building on this evidence, we argue that most models of decision-making, and metacognition, are intractable from a computational perspective. To be plausible, future theories of decision-making will need to take into account both the resources required for implementing the computations implied by the theory, and the resource constraints imposed on the decision-maker by biology.

On numerical cognition and the inexistence of an innate concept of number but the existence of an innate concept of quantity

Tom Verguts, Qi Chen, Numerical Cognition: Learning Binds Biology to Culture, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 913-914, DOI: 10.1016/j.tics.2017.09.004.

First, we address the issue of which quantity representations are innate. Second, we consider the role of the number list, whose characteristics are no doubt highly culturally dependent.

Layered learning: how to learn hierarchically more complex behaviors based on simpler ones, applied to robot soccer

Patrick MacAlpine, Peter Stone, Overlapping layered learning, Artificial Intelligence, Volume 254, 2018, Pages 21-43, DOI: 10.1016/j.artint.2017.09.001.

Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors. A key feature of layered learning is that higher layers directly depend on the learned lower layers. In its original formulation, lower layers were frozen prior to learning higher layers. This article considers a major extension to the paradigm that allows learning certain behaviors independently, and then later stitching them together by learning at the “seams” where their influences overlap. The UT Austin Villa 2014 RoboCup 3D simulation team, using such overlapping layered learning, learned a total of 19 layered behaviors for a simulated soccer-playing robot, organized both in series and in parallel. To the best of our knowledge this is more than three times the number of layered behaviors in any prior layered learning system. Furthermore, the complete learning process is repeated on four additional robot body types, showcasing its generality as a paradigm for efficient behavior learning. The resulting team won the RoboCup 2014 championship with an undefeated record, scoring 52 goals and conceding none. This article includes a detailed experimental analysis of the team’s performance and the overlapping layered learning approach that led to its success.

Interesting study about the concepts to be taught in control system engineering

R. M. Reck, Common Learning Objectives for Undergraduate Control Systems Laboratories,IEEE Transactions on Education, vol. 60, no. 4, pp. 257-264, DOI: 10.1109/TE.2017.2681624.

Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this paper, a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories were identified. A panel of 40 control systems faculty members completed a multi-round Delphi survey to bring them toward consensus on the common aspects of their laboratories. These panelists identified 15 laboratory objectives, 26 concepts, and 15 components common to their laboratories. Then an 45 additional faculty members and practitioners completed a follow-up survey to gather feedback on the results. In both surveys, each participant rated the importance of each item. While average ratings differed slightly between the two groups, the order in which the items were ranked was similar. Important examples of common learning objectives include connecting theory to what is implemented in the laboratory, designing controllers, and modeling systems. The most common component in both groups was MathWorks software. Some of the common concepts include block diagrams, stability, and PID control. Defining common aspects of undergraduate control systems laboratories enables common development, detailed comparisons, and simplified adaptation of equipment and experiments between campuses and programs.

An interesting simulation educational software for control systems engineering based on controlling a quadrotor

S. Khan, M. H. Jaffery, A. Hanif and M. R. Asif, Teaching Tool for a Control Systems Laboratory Using a Quadrotor as a Plant in MATLAB, IEEE Transactions on Education, vol. 60, no. 4, pp. 249-256, DOI: 10.1109/TE.2017.2653762.

This paper presents a MATLAB-based application to teach the guidance, navigation, and control concepts of a quadrotor to undergraduate students, using a graphical user interface (GUI) and 3-D animations. The Simulink quadrotor model is controlled by a proportional integral derivative controller and a linear quadratic regulator controller. The GUI layout’s many components can be easily programmed to perform various experiments by considering the simulation of the quadrotor as a plant; it incorporates control systems (CS) fundamentals such as time domain response, transfer function and state-space form, pole-zero location, root locus, frequency domain response, steady-state error, position and disturbance response, controller design and tuning, unity, and the use of a Kalman filter as a feedback sensor. 3-D animations are used to display the quadrotor flying in any given condition selected by the user. For each simulation, users can view the output response in the form of 3-D animations, and can run time plots. The quadrotor educational tool (QET) helps students in the CS laboratory understand basic CS concepts. The QET was evaluated based on student feedback, grades, satisfaction, and interest in CS.

Qualitative maps for mobile robots

Jennifer Padgett, Mark Campbell, Probabilistic qualitative mapping for robots, Robotics and Autonomous Systems, Volume 98, 2017, Pages 292-306, DOI: 10.1016/j.robot.2017.09.013.

A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments.

The security problems of ROS

Bernhard Dieber, Benjamin Breiling, Sebastian Taurer, Severin Kacianka, Stefan Rass, Peter Schartner, Security for the Robot Operating System, Robotics and Autonomous Systems,
Volume 98, 2017, Pages 192-203, DOI: 10.1016/j.robot.2017.09.017.

Future robotic systems will be situated in highly networked environments where they communicate with industrial control systems, cloud services or other systems at remote locations. In this trend of strong digitization of industrial systems (also sometimes referred to as Industry 4.0), cyber attacks are an increasing threat to the integrity of the robotic systems at the core of this new development. It is expected, that the Robot Operating System (ROS) will play an important role in robotics outside of pure research-oriented scenarios. ROS however has significant security issues which need to be addressed before such products should reach mass markets. In this paper we present the most common vulnerabilities of ROS, attack vectors to exploit those and several approaches to secure ROS and similar systems. We show how to secure ROS on an application level and describe a solution which is integrated directly into the ROS core. Our proposed solution has been implemented and tested with recent versions of ROS, and adds security to all communication channels without being invasive to the system kernel itself.

Interesting survey on Visual SLAM without filtering and of its future lines of research

Georges Younes, Daniel Asmar, Elie Shammas, John Zelek, Keyframe-based monocular SLAM: design, survey, and future directions, Robotics and Autonomous Systems, Volume 98, 2017, Pages 67-88, DOI: 10.1016/j.robot.2017.09.010.

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery.

A theoretical framework based on hybrid models and logical verification to prove the guarantees for obstacle avoidance in mobile robot navigation

Stefan Mitsch, Khalil Ghorbal, David Vogelbacher, and André Platzer, Formal verification of obstacle avoidance and navigation of ground robots, The International Journal of Robotics Research Vol 36, Issue 12, pp. 1312 – 1340, DOI: 0.1177/0278364917733549.

This article answers fundamental safety questions for ground robot navigation: under which circumstances does which control decision make a ground robot safely avoid obstacles? Unsurprisingly, the answer depends on the exact formulation of the safety objective, as well as the physical capabilities and limitations of the robot and the obstacles. Because uncertainties about the exact future behavior of a robot’s environment make this a challenging problem, we formally verify corresponding controllers and provide rigorous safety proofs justifying why the robots can never collide with the obstacle in the respective physical model. To account for ground robots in which different physical phenomena are important, we analyze a series of increasingly strong properties of controllers for increasingly rich dynamics and identify the impact that the additional model parameters have on the required safety margins. We analyze and formally verify: (i) static safety, which ensures that no collisions can happen with stationary obstacles; (ii) passive safety, which ensures that no collisions can happen with stationary or moving obstacles while the robot moves; (iii) the stronger passive-friendly safety, in which the robot further maintains sufficient maneuvering distance for obstacles to avoid collision as well; and (iv) passive orientation safety, which allows for imperfect sensor coverage of the robot, i.e., the robot is aware that not everything in its environment will be visible. We formally prove that safety can be guaranteed despite sensor uncertainty and actuator perturbation. We complement these provably correct safety properties with liveness properties: we prove that provably safe motion is flexible enough to let the robot navigate waypoints and pass intersections. To account for the mixed influence of discrete control decisions and the continuous physical motion of the ground robot, we develop corresponding hybrid system models and use differential dynamic logic theorem-proving techniques to formally verify their correctness. Since these models identify a broad range of conditions under which control decisions are provably safe, our results apply to any control algorithm for ground robots with the same dynamics. As a demonstration, we also synthesize provably correct runtime monitor conditions that check the compliance of any control algorithm with the verified control decisions.