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I'll keep an eye on you: Home robot companion for elderly people with cognitive impairment

I'll keep an eye on you: Home robot companion for elderly pe...

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IEEE International Conference on Systems, Man and Cybernetics

作者： H.-M. Gross Ch. Schroeter S. Mueller M. Volkhardt E. Einhorn A. Bley T. Langner Ch. Martin M. Merten Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany MetraLaboratories GmbH Ilmenau Germany

The paper gives an overview of the progress in developing a socially assistive home robot companion for elderly people with mild cognitive impairment (MCI) living alone at home. The spectrum of required assistive functionalities of such a robot companion is broad and reaches from reminding functions (e.g. taking medication or drinking) and cognitive stimulation exercises, via mobile videophony with relatives or caregivers, up to the detection and evaluation of critical situations, like falls. The paper is addressing several aspects of our work as part of the European FP7 project “CompanionAble”, as for example the developed robot hardware and its software and control architecture, the implemented skills for robust user detection and tracking and user-centered navigation in the home environment, and reports on already conducted and still ongoing functionality testings and pending usability studies with the end-user target groups (the elderly, relatives, caregivers).

关键词： Navigation Mobile robots Robot vision systems Cameras Robot kinematics

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Progress in developing a socially assistive mobile home robot companion for the elderly with mild cognitive impairment

Progress in developing a socially assistive mobile home robo...

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： H.-M. Gross Ch. Schroeter S. Mueller M. Volkhardt E. Einhorn A. Bley Ch. Martin T. Langner M. Merten Neuroinformatics and Cognitive Robotics laboratory Ilmenau University of Technology Ilmenau Germany Metra Laboratories Robotics GmbH Ilmenau Ilmenau Germany

The paper is addressing several aspects of our work as part of the European FP7 project ¿CompanionAble¿ and gives an overview of the progress in developing a socially assistive home robot companion for elderly people with mild cognitive impairment (MCI) living alone at home. The spectrum of required assistive functionalities and services that have been specified by the different end-user target groups of such a robot companion (the elderly, relatives, caregivers) is manifold. It reaches from situation-specific, intelligent reminding (e.g. taking medication or drinking) and cognitive stimulation, via mobile videophony with relatives or caregivers, up to the autonomous detection of dangerous situations, like falls, and their evaluation by authorized persons via mobile telepresence. From the beginning, our approach has been focused on long-term and everyday suitability and low-cost producibility as important prerequisites for the marketability of the robot companion. Against this background, the paper presents the main system requirements derived from user studies, the consequences for the hardware design and functionality of the robot companion, its system architecture, a key technology for HRI in home environments - the autonomous user tracking and searching, up to the results of already conducted and ongoing functionality tests and upcoming user studies.

关键词： Robot sensing systems Navigation Senior citizens Robot kinematics Mobile robots Collision avoidance

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, (ADPRL)

作者： Alexander Hans Siegmund Duell Steffen Udluft Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Munich Germany

With the development of data-efficient reinforcement learning (RL) methods, a promising data-driven solution for optimal control of complex technical systems has become available. For the application of RL to a technical system, it is usually required to evaluate a policy before actually applying it to ensure it operates the system safely and within required performance bounds. In benchmark applications one can use the system dynamics directly to measure the policy quality. In real applications, however, this might be too expensive or even impossible. Being unable to evaluate the policy without using the actual system hinders the application of RL to autonomous controllers. As a first step toward agent self-assessment, we deal with discrete MDPs in this paper. We propose to use the value function along with its uncertainty to assess a policy's quality and show that, when dealing with an MDP estimated from observations, the value function itself can be misleading. We address this problem by determining the value function's uncertainty through uncertainty propagation and evaluate the approach using a number of benchmark applications.

关键词： Uncertainty Equations Markov processes Benchmark testing Approximation algorithms Machine learning Histograms

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Ensembles of Neural Networks for Robust Reinforcement Learning

Ensembles of Neural Networks for Robust Reinforcement Learni...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Alexander Hans Steffen Udluft Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany Intelligent Systems and Control Corporate Technology Siemens AG Germany

Reinforcement learning algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, their training and the validation of final policies can be cumbersome as neural networks can suffer from problems like local minima or over fitting. When using iterative methods, such as neural fitted Q-iteration, the problem becomes even more pronounced since the network has to be trained multiple times and the training process in one iteration builds on the network trained in the previous iteration. Therefore errors can accumulate. In this paper we propose to use ensembles of networks to make the learning process more robust and produce near-optimal policies more reliably. We name various ways of combining single networks to an ensemble that results in a final ensemble policy and show the potential of the approach using a benchmark application. Our experiments indicate that majority voting is superior to Q-averaging and using heterogeneous ensembles (different network topologies) is advisable.

关键词： Training Function approximation Artificial neural networks Network topology Approximation algorithms Neurons

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Learning to navigate through crowded environments

Learning to navigate through crowded environments

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IEEE International Conference on robotics and Automation (ICRA)

作者： Peter Henry Christian Vollmer Brian Ferris Dieter Fox Department of Computer Science & Engineering University of Washington Seattle WA USA Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Germany

The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths. Since robots have only limited sensing, we extend existing IRL methods to the case of partially observable environments. We demonstrate the capabilities of our approach using a realistic crowd flow simulator in which we modeled multiple scenarios in crowded environments. We show that our planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.

关键词： Navigation Learning Cost function Mobile robots Airports Robot sensing systems Gaussian processes robotics and automation USA Councils Computer science

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TOOMAS: Interactive Shopping Guide robots in everyday use - final implementation and experiences from long-term field trials

TOOMAS: Interactive Shopping Guide robots in everyday use - ...

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2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： H.-M. Gross H. Boehme Ch. Schroeter S. Mueller A. Koenig E. Einhorn Ch. Martin M. Merten A. Bley Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany University of Applied Sciences Dresden Germany Metra Laboratories Robotics GmbH Ilmenau Ilmenau Germany

The paper gives a comprehensive overview of our Shopping Guide project, which aims at the development of interactive mobile shopping companion robots for everyday use in challenging operating environments such as home improvement stores. It is spanning an arc from the expectations and requirements of store owners and customers, via the challenges of the shopping scenario and the operating environment, the implemented functionality of the shopping guide robots, up to the results of long-term field trials. The field trials started in April 2008 and still ongoing aim at studying whether and how a group of interactive mobile shopping guide robots can operate completely autonomously in such everyday environments and how they are accepted by uninstructed customers. In these field trials, where nine robotic shopping guides traveled together 2187 kilometers in three different home improvement stores in Germany, more than 8,600 customers were successfully guided to the locations of their products of choice. With the successful development of these shopping guide robots, a further important step towards assistive robotics for daily use has been done.

关键词： Mobile robots cognitive robotics Intelligent robots Employee welfare USA Councils Navigation Design engineering Payloads Hardware Interactive systems

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Task-level imitation learning using variance-based movement optimization

Task-level imitation learning using variance-based movement ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Manuel Muhlig Michael Gienger Sven Hellbach Jochen J. Steil Christian Goerick Honda Research Institute Europe GmBH Offenbach Germany Research Institute of Cognition and Robotics Bielefeld University Bielefeld Germany Neuroinformatics and Cognitive Robotics Laboratory Technical University Ilmenau Ilmenau Germany

Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks.

关键词： Humanoid robots Service robots robotics and automation Humans Glass Europe cognitive robotics Encoding Collision avoidance Biological information theory

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A sensor-independent approach to RBPF SLAM - Map Match SLAM applied to visual mapping

A sensor-independent approach to RBPF SLAM - Map Match SLAM ...

引用

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Christof Schroeter Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

ISBN: (纸本)9781424420575

In this paper, we present the application of our generic, sensor-independent Map Match SLAM framework to visual mapping. In our previous work , we have introduced the map match SLAM approach for mapping with sonar range readings: Extending the grid-based Rao-Blackwellized particle filter SLAM approach, in Map Match SLAM, a local map is maintained by each particle in addition to the global map. The local map is used to represent the most recent observations, and weighting of the particles is done based on the compliance of the local and the global map. In this paper, we show how RBPF SLAM can also be applied for mapping and path reconstruction with a stereo camera or a single monocular camera, respectively. By mapping with completely different sensors such as sonar, stereo, or monocular cameras, we prove the wide range applicability of RBPF SLAM and our map match SLAM computational framework.

关键词： Robot sensing systems Robots Robot kinematics Cameras Robot vision systems Three dimensional displays Sonar

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Whom to talk to? Estimating user interest from movement trajectories

Whom to talk to? Estimating user interest from movement traj...

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17th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2008), vol.2

作者： Steffen Muller Sven Hellbach Erik Schaffernicht Antje Ober Andrea Scheidig Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

Correctly identifying people who are interested in an interaction with a mobile robot is an essential task for a smart Human-Robot Interaction. In this paper an approach is presented for selecting suitable trajectory features in a task specific manner from a huge amount of different forms of possible representations. Different sub-sampling techniques are proposed to generate trajectory sequences from which features are extracted. The trajectory data was generated in real world experiments that include extensive user interviews to acquire information about user behaviors and intentions. Using those feature vectors in a classification method enables the robot to estimate the user's interaction interest. For generating low-dimensional feature vectors, a common method, the Principle Component Analysis, is applied. The selection and combination of useful features out of a set of possible features is carried out by an information theoretic approach based on the Mutual Information and Joint Mutual Information with respect to the user's interaction interest. The introduced procedure is evaluated with neural classifiers, which are trained with the extracted features of the trajectories and the user behavior gained by observation as well as user interviewing. The results achieved indicate that an estimation of the user's interaction interest using trajectory information is feasible.

关键词： Robots

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A graph matching technique for an appearance-based, visual SLAM-approach using Rao-Blackwellized Particle Filters

A graph matching technique for an appearance-based, visual S...

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2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Alexander Koenig Jens Kessler Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

ISBN: (纸本)9781424420575

In continuation of our previous work on visual, appearance-based localization in manually built maps in this paper we present a novel appearance-based, visual SLAM approach. The essential contribution of this work is, an adaptive sensor model which is estimated online and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based, visual localization concept with a Rao-Blackwellized Particle Filter (RBPF) as state estimator to a real-world suitable, online SLAM approach. In our system, each RBPF particle incrementally constructs its own graph-based environment model which is labeled with visual appearance features (extracted from panoramic 360deg snapshots of the environment) and the estimated poses of the places where the snapshots were captured. The essential advantages of this appearance-based SLAM approach are its low memory and computing-time requirements. Therefore, the algorithm is able to perform in real-time. Finally, we present the results of SLAM experiments in two challenging environments that investigate the stability and localization accuracy of this SLAM technique.

关键词： Robots Visualization Robot sensing systems Computational modeling Adaptation model Feature extraction Trajectory

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