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

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

Long-term robot motion planning for active sound source localization with Monte Carlo tree search

International audienceWe consider the problem of controlling a mobile robot in order to localize a sound source. A microphone array can provide the robot with information on source localization. By combining this information with the movements of the robot, the localization accuracy can be improved. However, random robot motion or short-term planning may not result in optimal localization. In this paper, we propose an optimal long-term robot motion planning algorithm for active source lo-calization. We introduce a Monte Carlo tree search (MCTS) method to find a sequence of robot actions that minimize the entropy of the belief on the source location. A tree of possible robot movements which balances between exploration and exploitation is first constructed. Then, the movement that leads to minimum uncertainty is selected and executed. Experiments and statistical results show the effectiveness of our proposed method on improving sound source localization in the long term compared to other motion planning methods

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation

Motion planning for robot audition

International audienceRobot audition refers to a range of hearing capabilities which help robots explore and understand their environment. Among them, sound source localization is the problem of estimating the location of a sound source given measurements of its angle of arrival with respect to a microphone array mounted on the robot. In addition, robot motion can help quickly solve the front-back ambiguity existing in a linear microphone array. In this article, we focus on the problem of exploiting robot motion to improve the estimation of the location of an intermittent and possibly moving source in a noisy and reverberant environment. We first propose a robust extended mixture Kalman filtering framework for jointly estimating the source location and its activity over time. Building on this framework, we then propose a long-term robot motion planning algorithm based on Monte Carlo tree search to find an optimal robot trajectory according to two alternative criteria: the Shannon entropy or the standard deviation of the estimated belief on the source location. These criteria are integrated over time using a discount factor. Experimental results show the robustness of the proposed estimation framework to false angle of arrival measurements within ±20◦ and 10% false source activity detection rate. The proposed robot motion planning technique achieves an average localization error 48.7% smaller than a one-step-ahead method. In addition, we compare the correlation between the estimation error and the two criteria, and investigate the effect of the discount factor on the performance of the proposed motion planning algorithm

Sound Source Localisation Through Active Audition

This paper presents a novel method for enabling a robot to determine the direction to a sound source through interacting with its environment. The method uses a new neural network, the Parameter-Less Self-Organizing Map algorithm, and reinforcement learning to achieve rapid and accurate response