The problem to be discussed here, is the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world model has to be adapted by the flow of sensor- samples without the possibility to stop this data-flow.Our experiments are done in a simulation environment as well as on a robot, called ALICE
Abstract — In this paper we address the problem of navi-gating an autonomous mobile robot on an unkn...
In this paper neural network representation for the Q-learning algorithm of a mobile robot is presen...
Reservoir Computing (RC) uses a randomly created recurrent neural network where only a linear readou...
This paper presents experiments with a Nomad 200 mobile robot, acquiring a sensor model of a specifi...
In this study we ran real time learning of multiple physical autonomous robots situated in a real dy...
In our research program, we are developing machine learning algorithms to enable a mobile robot to b...
Abstract—This paper presents an adaptive method that allows mobile robots to learn cognitive maps of...
This paper presents and experimentally validates a concept of end-to-end imitation learning for auto...
Abstract:- This paper describes a new neural network able to adapt itself, both its parameters and i...
I present first results on COLUMBUS, an autonomous mobile robot. COLUMBUS operates in initially unkn...
This work has been partially supported by the Neural Nets Project of the Italian National Council of...
© 2019 IEEE. The paper is concerned with the autonomous navigation of mobile robot from the current ...
Loffler A, Klahold J, Rückert U. Artificial neural networks for autonomous robot control: reflective...
Autonomous robots must be able to learn and maintain models of their environments. In this context, ...
This paper describes the saliency-based scene memory model of a mobile robot in which objects in sal...
Abstract — In this paper we address the problem of navi-gating an autonomous mobile robot on an unkn...
In this paper neural network representation for the Q-learning algorithm of a mobile robot is presen...
Reservoir Computing (RC) uses a randomly created recurrent neural network where only a linear readou...
This paper presents experiments with a Nomad 200 mobile robot, acquiring a sensor model of a specifi...
In this study we ran real time learning of multiple physical autonomous robots situated in a real dy...
In our research program, we are developing machine learning algorithms to enable a mobile robot to b...
Abstract—This paper presents an adaptive method that allows mobile robots to learn cognitive maps of...
This paper presents and experimentally validates a concept of end-to-end imitation learning for auto...
Abstract:- This paper describes a new neural network able to adapt itself, both its parameters and i...
I present first results on COLUMBUS, an autonomous mobile robot. COLUMBUS operates in initially unkn...
This work has been partially supported by the Neural Nets Project of the Italian National Council of...
© 2019 IEEE. The paper is concerned with the autonomous navigation of mobile robot from the current ...
Loffler A, Klahold J, Rückert U. Artificial neural networks for autonomous robot control: reflective...
Autonomous robots must be able to learn and maintain models of their environments. In this context, ...
This paper describes the saliency-based scene memory model of a mobile robot in which objects in sal...
Abstract — In this paper we address the problem of navi-gating an autonomous mobile robot on an unkn...
In this paper neural network representation for the Q-learning algorithm of a mobile robot is presen...
Reservoir Computing (RC) uses a randomly created recurrent neural network where only a linear readou...