A new device laboratory: Experimental validation

Postprint (published version

ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution

We consider the problem of efficient blackbox optimization over a large hybrid search space, consisting of a mixture of a high dimensional continuous space and a complex combinatorial space. Such examples arise commonly in evolutionary computation, but also more recently, neuroevolution and architecture search for Reinforcement Learning (RL) policies. Unfortunately however, previous mutation-based approaches suffer in high dimensional continuous spaces both theoretically and practically. We thus instead propose ES-ENAS, a simple joint optimization procedure by combining Evolutionary Strategies (ES) and combinatorial optimization techniques in a highly scalable and intuitive way, inspired by the one-shot or supernet paradigm introduced in Efficient Neural Architecture Search (ENAS). Through this relatively simple marriage between two different lines of research, we are able to gain the best of both worlds, and empirically demonstrate our approach by optimizing BBOB functions over hybrid spaces as well as combinatorial neural network architectures via edge pruning and quantization on popular RL benchmarks. Due to the modularity of the algorithm, we also are able incorporate a wide variety of popular techniques ranging from use of different continuous and combinatorial optimizers, as well as constrained optimization.Comment: 22 pages. See https://github.com/google-research/google-research/tree/master/es_enas for associated cod

Rotorigami: A rotary origami protective system for robotic rotorcraft

Applications of aerial robots are progressively expanding into complex urban and natural environments. Despite remarkable advancements in the field, robotic rotorcraft is still drastically limited by the environment in which they operate. Obstacle detection and avoidance systems have functionality limitations and substantially add to the computational complexity of the onboard equipment of flying vehicles. Furthermore, they often cannot identify difficult-to-detect obstacles such as windows and wires. Robustness to physical contact with the environment is essential to mitigate these limitations and continue mission completion. However, many current mechanical impact protection concepts are either not sufficiently effective or too heavy and cumbersome, severely limiting the flight time and the capability of flying in constrained and narrow spaces. Therefore, novel impact protection systems are needed to enable flying robots to navigate in confined or heavily cluttered environments easily, safely, and efficiently while minimizing the performance penalty caused by the protection method. Here, we report the development of a protection system for robotic rotorcraft consisting of a free-to-spin circular protector that is able to decouple impact yawing moments from the vehicle, combined with a cyclic origami impact cushion capable of reducing the peak impact force experienced by the vehicle. Experimental results using a sensor-equipped miniature quadrotor demonstrated the impact resilience effectiveness of the Rotary Origami Protective System (Rotorigami) for a variety of collision scenarios. We anticipate this work to be a starting point for the exploitation of origami structures in the passive or active impact protection of robotic vehicles

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys

In this thesis is presented a novel approach to model, control, and planning the motion of a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a nonholonomic cart (York mail trolley) in a loosely structured environment. The method is based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its kinematics properties. In consequence, two robot configurations are produced by the task of grasping and ungrasping the load, they are: the single-robot configuration and the robot-trolley configuration. Furthermore, in order to comply with the general planar motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for each configuration, the robot has been provided with two motorized steerable wheels in order to have a flexible platform able to adapt to these restrictions. [Continues.

Configuration Recognition, Communication Fault Tolerance and Self-reassembly for the CKBot

We present and experimentally verify novel methods for increasing the generality of control, autonomy and reliability for modular robotic systems. In particular, we demonstrate configuration recognition, distributed communication fault tolerance, and the organization and control of self-reassembly with the Connector Kinetic roBot (CKBot). The primary contribution of this work is the presentation and experimental verification of these innovative methods that are general and applicable to other modular robotic systems. We describe our CKBot system and compare it to other similar, state-of-the-art modular robotic systems. Our description and comparison highlights various design developments, features, and notable achievements of these systems. We present work on isomorphic configuration recognition with CKBot. Here, we utilize basic principles from graph theory to create and implement an algorithm on CKBot that automatically recognizes modular robot configurations. In particular, we describe how comparing graph spectra of configuration matrices can be used to find a permutation matrix that maps a given configuration to a known one. If a configuration is matched to one in a library of stored gaits, a permutation mapping is applied and the corresponding coordinated control for locomotion is executed. An implementation of the matching algorithm with small configurations of CKBot configurations that can be rearranged during runtime is presented. We also present work on a distributed fault-tolerance algorithm used to control CKBot configurations. Here, we use a triple modular redundancy approach for CKBot units to collectively vote on observations and execute commands in the presence of infrared (IR) communication failures. In our implementation, we broadcast infrared signals to modules which collaboratively vote on a majority course of action. Various gait selections for a seven module caterpillar and sixteen module quadruped with faulty subsets of IR receivers have been verified to demonstrate the algorithm\u27s robustness. Lastly, we present work on the communication hierarchy and control state machine for the Self-reassembly After Explosion (SAE) robot. Here, we discuss the interaction and integration of the various sensory inputs and control outputs implemented for camera-guided self-reassembly with CKBot. This section describes the overall communication system and reassembly sequence planning after a group of CKBot clusters is kicked apart

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

Visual attention and swarm cognition for off-road robots

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2011Esta tese aborda o problema da modelação de atenção visual no contexto de robôs autónomos todo-o-terreno. O objectivo de utilizar mecanismos de atenção visual é o de focar a percepção nos aspectos do ambiente mais relevantes à tarefa do robô. Esta tese mostra que, na detecção de obstáculos e de trilhos, esta capacidade promove robustez e parcimónia computacional. Estas são características chave para a rapidez e eficiência dos robôs todo-o-terreno. Um dos maiores desafios na modelação de atenção visual advém da necessidade de gerir o compromisso velocidade-precisão na presença de variações de contexto ou de tarefa. Esta tese mostra que este compromisso é resolvido se o processo de atenção visual for modelado como um processo auto-organizado, cuja operação é modulada pelo módulo de selecção de acção, responsável pelo controlo do robô. Ao fechar a malha entre o processo de selecção de acção e o de percepção, o último é capaz de operar apenas onde é necessário, antecipando as acções do robô. Para fornecer atenção visual com propriedades auto-organizadas, este trabalho obtém inspiração da Natureza. Concretamente, os mecanismos responsáveis pela capacidade que as formigas guerreiras têm de procurar alimento de forma auto-organizada, são usados como metáfora na resolução da tarefa de procurar, também de forma auto-organizada, obstáculos e trilhos no campo visual do robô. A solução proposta nesta tese é a de colocar vários focos de atenção encoberta a operar como um enxame, através de interacções baseadas em feromona. Este trabalho representa a primeira realização corporizada de cognição de enxame. Este é um novo campo de investigação que procura descobrir os princípios básicos da cognição, inspeccionando as propriedades auto-organizadas da inteligência colectiva exibida pelos insectos sociais. Logo, esta tese contribui para a robótica como disciplina de engenharia e para a robótica como disciplina de modelação, capaz de suportar o estudo do comportamento adaptável.Esta tese aborda o problema da modelação de atenção visual no contexto de robôs autónomos todo-o-terreno. O objectivo de utilizar mecanismos de atenção visual é o de focar a percepção nos aspectos do ambiente mais relevantes à tarefa do robô. Esta tese mostra que, na detecção de obstáculos e de trilhos, esta capacidade promove robustez e parcimónia computacional. Estas são características chave para a rapidez e eficiência dos robôs todo-o-terreno. Um dos maiores desafios na modelação de atenção visual advém da necessidade de gerir o compromisso velocidade-precisão na presença de variações de contexto ou de tarefa. Esta tese mostra que este compromisso é resolvido se o processo de atenção visual for modelado como um processo auto-organizado, cuja operação é modulada pelo módulo de selecção de acção, responsável pelo controlo do robô. Ao fechar a malha entre o processo de selecção de acção e o de percepção, o último é capaz de operar apenas onde é necessário, antecipando as acções do robô. Para fornecer atenção visual com propriedades auto-organizadas, este trabalho obtém inspi- ração da Natureza. Concretamente, os mecanismos responsáveis pela capacidade que as formi- gas guerreiras têm de procurar alimento de forma auto-organizada, são usados como metáfora na resolução da tarefa de procurar, também de forma auto-organizada, obstáculos e trilhos no campo visual do robô. A solução proposta nesta tese é a de colocar vários focos de atenção encoberta a operar como um enxame, através de interacções baseadas em feromona. Este trabalho representa a primeira realização corporizada de cognição de enxame. Este é um novo campo de investigação que procura descobrir os princípios básicos da cognição, ins- peccionando as propriedades auto-organizadas da inteligência colectiva exibida pelos insectos sociais. Logo, esta tese contribui para a robótica como disciplina de engenharia e para a robótica como disciplina de modelação, capaz de suportar o estudo do comportamento adaptável.Fundação para a Ciência e a Tecnologia (FCT,SFRH/BD/27305/2006); Laboratory of Agent Modelling (LabMag