The Parameter-Less Self-Organizing Map algorithm

The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighbourhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.Comment: 29 pages, 27 figures. Based on publication in IEEE Trans. on Neural Network

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Microphone array signal processing for robot audition

Robot audition for humanoid robots interacting naturally with humans in an unconstrained real-world environment is a hitherto unsolved challenge. The recorded microphone signals are usually distorted by background and interfering noise sources (speakers) as well as room reverberation. In addition, the movements of a robot and its actuators cause ego-noise which degrades the recorded signals significantly. The movement of the robot body and its head also complicates the detection and tracking of the desired, possibly moving, sound sources of interest. This paper presents an overview of the concepts in microphone array processing for robot audition and some recent achievements

Using FPGA for visuo-motor control with a silicon retina and a humanoid robot

The address-event representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI chips. The event information is transferred using a high speed digital parallel bus. This paper present an experiment based on AER for visual sensing, processing and finally actuating a robot. The AER output of a silicon retina is processed by an AER filter implemented into a FPGA to produce a mimicking behaviour in a humanoid robot (The RoboSapiens V2). We have implemented the visual filter into the Spartan II FPGA of the USB-AER platform and the Central Pattern Generator (CPG) into the Spartan 3 FPGA of the AER-Robot platform, both developed by authors.Unión Europea IST-2001-34124 (CAVIAR)Ministerio de Ciencia y Tecnología TIC-2003-08164-C03-0

Active Audition for Robots using Parameter-Less Self-Organising Maps

How can a robot become aware of its surroundings? How does it create its own subjective, inner representation of the real world, so that relationships in the one are reflected in the other? It is well known that structures analogous to Self-Organising Maps (SOM) are involved with this task in animals, and this thesis undertakes to explore if and how a similar approach can be success- fully applied in robotics. In order to study the environment-to-abstraction mapping with a minimum of guidance from directed learning and built-in design assumptions, this thesis examines the active audition task in which a system must determine the direction of a sound source and orient towards it, both in horizontal and vertical direction. Previous explanations of directional hearing in animals, and the implementation of directional hearing algorithms in robots have tended to focus on the two best known directional clues; the intensity and time differences. This thesis hypothesises that it is advantageous to use a synergy of a wider range of metrics, namely the phase and relative intensity difference. A solution to the active audition problem is proposed based on the Parameter- Less Self-Organising Map (PLSOM), a new algorithm also introduced in this thesis. The PLSOM is used to extract patterns from a high-dimensional input space to a low-dimensional output space. In this application the output space is mapped to the correct motor command for turning towards the source and focusing attention on the selected source by filtering unwanted noise. The dimension-reducing capability of the PLSOM enables the use of more than just two directional clues for computation of the direction. This thesis presents the new PLSOM algorithm for SOM training and quantifies its performance relative to the ordinary SOM algorithm. The mathematical correctness of the PLSOM is demonstrated and the properties and some applications of this new algorithm are examined, notably in automatically modelling a robot's surroundings in a functional form: Inverse Kinematics (IK). The IK problem is related in principle to the active audition problem - functional rather than abstract representation of reality - but raises some new questions of how to use this internal representation in planning and execution of movements. The PLSOM is also applied to classification of high-dimensional data and model-free chaotic time series prediction. A variant of Reinforcement Learning based on Q-Learning is devised and tested. This variant solves some problems related to stochastic reward functions. A mathematical proof of correct state-action pairing is devised