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MODELING AND TESTING PROXEMIC behavior FOR HUMANOID ROBOTS

INTERNATIONAL JOURNAL OF HUMANOID robotics 2012年第4期9卷 1250028.1-1250028.24页

作者： Torta, Elena Cuijpers, Raymond H. Juola, James F. Van Der Pol, David Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands

Humanoid robots that share the same space with humans need to be socially acceptable and effective as they interact with people. In this paper we focus our attention on the definition of a behavior-based robotic architecture that (1) allows the robot to navigate safely in a cluttered and dynamically changing domestic environment and (2) encodes embodied non-verbal interactions: the robot respects the users personal space (PS) by choosing the appropriate distance and direction of approach. The model of the PS is derived from human-robot interaction tests, and it is described in a convenient mathematical form. The robot's target location is dynamically inferred through the solution of a Bayesian filtering problem. The validation of the overall behavioral architecture shows that the robot is able to exhibit appropriate proxemic behavior.

关键词： behavior based robotics proxemics user models social robotics context awareness particle filter

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SLAM-based Spatial Memory for behavior-based Robots

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IFAC-PapersOnLine 2015年第19期48卷 195-202页

作者： Jiang, Shu Arkin, Ronald C. School of Interactive Computing Georgia Institute of Technology AtlantaGA30308 United States

Knowledge is essential for an autonomous robot to act intelligently when tasked with a mission. With recent leaps of progress, the paradigm of SLAM (Simultaneous Localization and Mapping) has emerged as an ideal source of spatial knowledge for autonomous robots. However, despite advancements in both paradigms of SLAM and robot control, research in the integration of these areas has been lacking and remained open to investigation. This paper presents an integration of SLAM into a behavior-based robotic system as a dynamically acquired spatial memory, which can be used to enable new behaviors and augment existing ones. The effectiveness of the integrated system is demonstrated with a biohazard search mission, where a robot is tasked to search and locate a biohazard within an unknown environment under a time constraint. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： robotics Biohazards Mapping Robots behavior based robotics behavior based robots Integrated systems Simultaneous localization and mapping SLAM (simultaneous localization and mapping) Spatial knowledge Time constraints Unknown environments

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behavior reuse and virtual sensors in the evolution of complex behavior architectures

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THEORY IN BIOSCIENCES 2001年第3-4期120卷 188-206页

作者： Duro, RJ Becerra, JA Santos, J Univ A Coruna Grp Sistemas Autononos La Coruna Spain

In this paper we discuss some of the new work we have been carrying out with the objective of making evolutionarily obtained behavior based architectures and modules for autonomous robots more standardized and interchangeable. The architectures contemplated here are based on a multiple behavior structure where all of the modules, as well as their interconnections, are automatically obtained through evolutionary processes. The main objective of this line of research is to obtain procedures that permit producing behavior based controllers that work on real robots operating in real environments as independently of the platform as possible. In this particular paper we will concentrate on different aspects regarding the inclusion of virtual sensors as a way to make improved use of the capabilities of the different platforms and on the reuse of behavior modules. This reuse will be contemplated within the same behavioral architecture and from the point of view of transferring behavior modules from one platform to a different one.

关键词： autonomy evolutionary robotics behavior reuse behavior based robotics artificial neural networks

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Robust One-Shot Robot Programming by Demonstration Using Entity-based Resources 26th

Robust One-Shot Robot Programming by Demonstration Using Ent...

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26th International Conference on robotics in Alpe-Adria-Danube Region (RAAD)

作者： Orendt, Eric M. Riedl, Michael Henrich, Dominik Univ Bayreuth Chair Appl Comp Sci Robot & Embedded Syst 3 D-95440 Bayreuth Germany

ISBN: (纸本)9783319612768;9783319612751

General purpose robots are established tools in a variety of industrial applications. An important goal in actual research is to transfer the advantages of these tools into more unstructured environments like househoulds or small and medium sized enterprises. One challenge in this field is to enable non-experts to use all the capabilities of a robot. This includes two aspects: Robots must be intuitive to program and robust to execute. The main contribution of this work is a novel programming approach, that concerns both aspects. Thus our system enables users to guide a robot kinesthetically through a task without prior knowledge. By observing resources in the workspace, the demonstrated task is encoded as a finite state machine (FSM). This FSM allows the reproduction of a task by the robot itself. Furthermore, our approach can integrate a deviation detection method to robustify task reproductions.

关键词： Programming by demonstration Entity actor system Finite state machines behavior based robotics Kinesthetic programming

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Modified Reinforcement Learning for Sequential Action behaviors and its Application to robotics

Modified Reinforcement Learning for Sequential Action Behavi...

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IEEE SOUTHEASTCON

作者： Robinson, Chris Univ Louisville Speed Sch Engn Dept Elect & Comp Engn Louisville KY 40292 USA

ISBN: (纸本)9781479965854

When developing a robot or other automaton, the efficacy of the agent is highly dependent on the performance of the behaviors which underpin the control system. Especially in the case of agents which must act in real world or disorganized environments, the design of robust behaviors can be both difficult and time consuming, and often requires the use of sensitive tuning. In response to this need, we present a behavioral, goal-oriented, reinforcement-based machine learning strategy which is flexible, simple to implement, and designed for application in real-world environments, but with the capability of software-based training. In this paper, we will explain our design paradigms, the formal implementation thereof, and the algorithm proper. We will show that the algorithm is able to emulate standard reinforcement learning within comparable training time, and to extend the capabilities thereof as well. We also demonstrate extension of learning beyond the scope of training examples, and present an example of a physical robot which learns a sequential action behavior by experimentation.

关键词： Machine learning Robot control Robots Reinforcement learning Algorithms Probabilistic learning behaviors behavior based robotics Operant Conditioning

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Situated Learning of Visual Robot behaviors 11

Situated Learning of Visual Robot Behaviors

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4th International Conference on Intelligent robotics and Applications (ICIRA 2011)

作者： Narayanan, Krishna Kumar Posada, Luis-Felipe Hoffmann, Frank Bertram, Torsten Tech Univ Dortmund Inst Control Theory & Syst Engn D-44227 Dortmund Germany

ISBN: (纸本)9783642254857

This paper proposes a new robot learning framework to acquire scenario specific autonomous behaviors by demonstration. We extract visual features from the demonstrated behavior examples in an indoor environment and transfer it onto an underlying set of scenario aware robot behaviors. Demonstrations are performed using an omnidirectional camera as training instances in different indoor scenarios are registered. The features that distinguish the environment are identified and are used to classify the traversing scenarios. Once the scenario is identified, a behavior model trained by means of artificial neural network pertaining to the specific scenario is learned. The generalization ability of the behavior model is evaluated for seen and unseen data. As a comparison, the behaviors attained using a monolithic general purpose model and its generalization ability against the former is evaluated. The experimental results on the mobile robot indicate the acquired behavior is robust and generalizes meaningful actions beyond the specifics presented during training.

关键词： visual navigation learning from demonstration situated learning behavior based robotics

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Agent-based simulation of robotic systems

Agent-based simulation of robotic systems

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作者： Williams, Manoleto Z. Naval Postgraduate School

学位级别：硕士

A goal and behavior agent layer Java Model was developed to simulate cruise, correct and avoid Control Modules in an autonomous agent (robot). The model was tested against a deterministic Figure of Merit (FOM) to predict a "best mix" of agents for the simplistic agent economy parameters given. Future works suggests validation of the model with real agents in a real economy.

关键词： Agent based Simulation Modeling and Simulation robotics Self-organization behavior based robotics

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