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An evolutionary simulated annealing algorithm for optimizing robotic task point ordering

An evolutionary simulated annealing algorithm for optimizing...

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ieee international symposium on Assembly and Manufacturing (ISAM)

作者： D. Barral J.-P. Perrin E. Dombre A. Liegeois L.I.R.M. UMR No 5506 C.N.R.S. Université Montpelher II Montpellier France Dassault Systèmes Suresnes France

High productivity requires that robot manipulators perform complex tasks in minimum time. The paper presents an evolutionary simulated annealing (ESA) algorithm for optimizing an important class of complex tasks of point-to-point moves, such as mechanical assembly, electronic component insertion, and spot welding. This algorithm combines the basic principles of two major heuristic search methods: simulated annealing and genetic algorithms. Indeed, these methods are commonly used to solve the well-known traveling salesman problem (TSP), and the point ordering problem in robotics is very similar to the TSP in mathematics. The three algorithms have been implemented in a computer-aided design (CAD) software system, CATIA. Experimental results show success factors for using ESA.

关键词： Simulated annealing Robots Design automation Productivity Manipulators Robotic assembly Electronic components Spot welding Search methods computational modeling

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Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

Robust Model-free Reinforcement Learning with Multi-objectiv...

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ieee international Conference on robotics and automation (ICRA)

作者： Matteo Turchetta Andreas Krause Sebastian Trimpe Learning and Adaptive Systems Group ETH Zurich Zürich Switzerland Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment. In real world applications, test conditions may differ substantially from the training scenario and, therefore, focusing on pure reward maximization during training may lead to poor results at test time. In these cases, it is important to trade-off between performance and robustness while learning a policy. While several results exist for robust, model-based RL, the model-free case has not been widely investigated. In this paper, we cast the robust, model-free RL problem as a multi-objective optimization problem. To quantify the robustness of a policy, we use delay margin and gain margin, two robustness indicators that are common in control theory. We show how these metrics can be estimated from data in the model-free setting. We use multi-objective Bayesian optimization (MOBO) to solve efficiently this expensive-to-evaluate, multi-objective optimization problem. We show the benefits of our robust formulation both in sim-to-real and pure hardware experiments to balance a Furuta pendulum.

关键词： Robustness Optimization Training Control theory computational modeling Learning (artificial intelligence) Bayes methods

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A visual-inertial approach for camera egomotion estimation and simultaneous recovery of scene structure

A visual-inertial approach for camera egomotion estimation a...

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ieee international symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS)

作者： Dominik Aufderheide Werner Krybus Institute for Computer Science Vision and Computational Intelligence CV&CI University of Applied Sciences South Westphalia Soest Germany

The estimation of a camera's egomotion is a highly desireable goal in many different application fields such as augmented reality (AR), visual navigation, robotics or entertainment. Especially for real-time modeling the former estimation of the camera trajectory is an elementary step towards the generation of three dimensional scene models. This paper presents a framework for simultaneous recovery of scene structure and camera motion by combining visual and inertial cues. For this purpose two different system designs are proposed: a loosely-coupled system and a monolithic design, which adapts ideas from non-linear state estimation as extended Kalman filtering (EKF) for structure and motion recovery.

关键词： Cameras Visualization Feature extraction Estimation Robot sensing systems Robustness Accelerometers

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An experimentation on creating a mental state transition network

An experimentation on creating a mental state transition net...

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international Conference on Information and automation (ICIA)

作者： Hua Xiang F. Ren S. Kuroiwa Peilin Jiang Department of Information Science and Intelligent Systems University of Tokushima Tokushima Japan Institute of Artificial Intelligence and Robotics Xi''an Jiaotong University Xi'an Shanxi China

Studies on the model of artificial psychology are the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a mental state transition network is presented and a psychological experiment based on the model is carried out. In this work, we hypothesize that internal mental movements of a human depend on a mental state transition network that is made up of seven discrete emotional states. And we propose a new method to predict the emotional transitional probability distribution by using psychological questionnaires. The transition probability distribution of the model is calculated through a psychological experiment and the model provided a relatively high precision rate of 0.872 for the 50 random data examples used in an experimentation.

关键词： Humans Psychology Cognitive science Hidden Markov models Artificial intelligence Intelligent robots Emotion recognition Probability distribution computational modeling Computer simulation

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A Survey on Ethical Principles of AI and Implementations

A Survey on Ethical Principles of AI and Implementations

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ieee symposium Series on computational intelligence (SSCI)

作者： Jianlong Zhou Fang Chen Adam Berry Mike Reed Shujia Zhang Siobhan Savage Data Science Institute University of Technology Sydney Sydney Australia Reejig Pty Ltd Sydney Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

AI has powerful capabilities in prediction, automation, planning, targeting, and personalisation. Generally, it is assumed that AI can enable machines to exhibit human-like intelligence, and is claimed to benefit to different areas of our lives. Since AI is fueled by data and is a distinct form of autonomous and self-learning agency, we are seeing increasing ethical concerns related to AI uses. In order to mitigate various ethical concerns, national and international organisations including governmental organisations, private sectors as well as research institutes have made extensive efforts by drafting ethical principles of AI, and having active discussions on ethics of AI within and beyond the AI community. This paper investigates these efforts with a focus on the identification of fundamental ethical principles of AI and their implementations. The review found that there is a convergence around limited principles and the most prevalent principles are transparency, justice and fairness, responsibility, non-maleficence, and privacy. The investigation suggests that ethical principles need to be combined with every stages of the AI lifecycle in the implementation to ensure that the AI system is designed, implemented and deployed in an ethical manner. Similar to ethical framework used in biomedical and clinical research, this paper suggests checklist-style questionnaires as benchmarks for the implementation of ethical principles of AI.

关键词： Artificial intelligence Ethics Standards Guidelines Face recognition Privacy Speech recognition

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Learning Reward Modalities for Human-Robot-Interaction in a Cooperative Training Task

Learning Reward Modalities for Human-Robot-Interaction in a ...

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ieee international Workshop on Robot and Human Communication (ROMAN)

作者： Anja Austermann Seiji Yamada Graduate University of Advanced Studies (SOKENDAI) Chiyoda Tokyo Japan National Institute of Information Chiyoda Tokyo Japan

This paper proposes a novel method of learning a users preferred reward modalities for human-robot interaction through solving a cooperative training task. A learning algorithm based on a combination of adaptable pre-trained hidden Markov models and a computational model of classical conditioning is outlined. In a training task, where the desired outcome is known by an AIBO pet robot as well as its human instructor, the robot can freely explore human reward behavior. By this method, the robot is able to learn situated, user-specific reward behavior in the different modalities such as gestures, speech and interaction using the robot's built-in sensors. After the training phase, the learned reward behavior can be used as a basis for reinforcement learning of more complex tasks. A preliminary experimental study is presented, which investigates on the effects of restricting possible reward modalities, when teaching a pet robot. The results of the experiments suggest that being able to provide reward freely makes users give more reward compared to a scenario, where reward modalities are restricted. Moreover, the experiments showed that even if a restriction in possible reward modalities is introduced, users tend to give reward that does not conform to the restriction.

关键词： Robot sensing systems Human robot interaction Hidden Markov models Educational robots Positron emission tomography robotics and automation Negative feedback Tactile sensors Cognitive robotics Speech

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Improved Simple Noise Filtering for Fixed Point Iteration-based Adaptive Controllers

Improved Simple Noise Filtering for Fixed Point Iteration-ba...

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international symposium on computational intelligence and Informatics

作者： Hamza Khan Hazem Issa József K. Tar Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Research and Innovation Center Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary

ISBN: (数字)9781728183404

ISBN: (纸本)9781728183411

For controlling dynamical systems certain controllers directly observe the physical response of the controlled systems and feed back the observed response for stabilization purposes. For instance, in robotics, where the rules of Classical Mechanics govern the behavior of the controlled system, this is the acceleration that is measured and fed back by the Acceleration Feedback Controllers. In quite different manner, the Fixed Point Iteration (FPI)-based adaptive controllers also feed back such observed information. If the driving forces act on a robot link through some deformable, elastic component, the relative order of the control task can be higher than two, and the system's response can be a higher order time-derivative of the observable coordinate. In this case the numerical derivatives of the noise-burdened measurement signals suffer from huge fluctuations due to which the need for the application of noise filtering techniques arises. Generally, any noise filtering corresponds to some “averaging” of the fresh, recent dynamic information with older, more or less “obsolete” one so it may corrupt the operation of the FPI-based control. The extent of this degradation depends on the “dynamics” of the nominal trajectory to be tracked, the nonlinearities in the dynamic model of the controlled system and cannot be tackled at high level generality. In the present paper a new, simple noise filtering technique is recommended for use in the FPI-based adaptive controller. Its corruption effect is investigated for a chirp signal independently of any dynamic control issue, then its application in the FPI-based adaptive control of a popular, nonlinear benchmarking system, the Duffing oscillator is studied via numerical simulations.

关键词： Noise measurement Informatics Adaptation models computational modeling Adaptive control Finite impulse response filters Filtering

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Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

Is That a Chair? Imagining Affordances Using Simulations of ...

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ieee international Conference on robotics and automation (ICRA)

作者： Hongtao Wu Deven Misra Gregory S. Chirikjian Laboratory for Computational Sensing and Robotics (LCSR) Johns Hopkins University Baltimore MD USA Physics Department Reed College Portland OR USA Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.

关键词： Robots Solid modeling Cognition Physics Three-dimensional displays Data models Geometry

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A Task-based Approach to Generate Optimal Software-Architecture for Intelligent Service Robots

A Task-based Approach to Generate Optimal Software-Architect...

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ieee international Workshop on Robot and Human Communication (ROMAN)

作者： Yu-Sik Park In-Young Ko Sooyong Park School of Engineering Information and Communications University Daejeon South Korea Sooyong Park Department of Computer Science Sogang University Seoul South Korea

In order to provide services more reliably, intelligent service robots need to consider various factors, such as their surrounding environments, user's changing requirements, and constrained resources. Most of the intelligent service robots are controlled based on a task-based control system, which generates a task plan that consists of a sequence of actions, and executes the actions by invoking the corresponding functions. However, this task-based control system did not seriously consider resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). If we consider these factors during the task generation time, the complexity of the plan may become unmanageable. Therefore, in this paper, we propose a mechanism for robots to efficiently use their resources on-demand. We define reusable software-architectures corresponding to each action of a task plan, and provide a way of using the limited resources by minimizing redundant software components. We conducted an experiment of this mechanism for an infotainment robot. The experiment shows the effectiveness of our mechanism.

关键词： Intelligent robots Service robots Control systems Robot control computational intelligence Intelligent networks Computer networks Bandwidth Orbital robotics Software reusability

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Realtime reinforcement learning for a real robot in the real environment

Realtime reinforcement learning for a real robot in the real...

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Proceedings of ieee/RSJ international Conference on Intelligent Robots and Systems. IROS '96

作者： T. Yamaguchi M. Masubuchi K. Fujihara M. Yachida Department of Systems Engineering Faculty of Engineering Science Osaka University Toyonaka Osaka Japan Osaka Daigaku Suita Osaka JP

For a real robot to acquire behaviors, it is important for it to learn in a real environment. Most reinforcement learning research has been made by simulation because real-environment learning requires large computation costs as well as a lot of time. Realizing reinforcement learning of a physical robot in a real environment requires both an adaptation for the diversity of possible situations and a high-speed learning method that can learn from fewer trials. This paper describes the realtime reinforcement learning for a real robot in the real environment based on the exploitation oriented reinforcement learning method where the learning cost is very small and has strict incrementality to realize realtime reinforcement learning with an automated sub-rewards generation method achieved by the abstraction of the task state to accelerate the learning process. Successive learning experiments in the real environment for the ball pushing task for the real robot are performed.

关键词： Virtual environment computational modeling Intelligent robots computational efficiency Learning systems Costs robotics and automation Artificial intelligence Computer simulation Systems engineering and theory

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