POSTMAN: Point of Sail Tacking for Maritime Autonomous Navigation

Waves apply significant forces to small boats, in particular when such vessels are moving at a high speed in severe sea conditions. In addition, small high-speed boats run the risk of diving with the bow into the next wave crest during operations in the wavelengths and wave speeds that are typical for shallow water. In order to mitigate the issues of autonomous navigation in rough water, a hybrid controller called POSTMAN combines the concept of POS (point of sail) tack planning from the sailing domain with a standard PID (proportional-integral-derivative) controller that implements reliable target reaching for the motorized small boat control task. This is an embedded, adaptive software controller that uses look-ahead sensing in a closed loop method to perform path planning for safer navigation in rough waters. State-of-the-art controllers for small boats are based on complex models of the vessel's kinematics and dynamics. They enable the vessel to follow preplanned paths accurately and can theoretically control all of the small boat s six degrees of freedom. However, the problems of bow diving and other undesirable incidents are not addressed, and it is questionable if a six-DOF controller with basically a single actuator is possible at all. POSTMAN builds an adaptive capability into the controller based on sensed wave characteristics. This software will bring a muchneeded capability to unmanned small boats moving at high speeds. Previously, this class of boat was limited to wave heights of less than one meter in the sea states in which it could operate. POSTMAN is a major advance in autonomous safety for small maritime craft

Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot

Reinhart F, Steil JJ. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. In: Procedia Technology. Vol 26. 2016: 12-19

Rare neural correlations implement robotic conditioning with delayed rewards and disturbances

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms

Learning How to Speak: Imitation-Based Refinement of Syllable Production in an Articulatory-Acoustic Model

Philippsen A, Reinhart F, Wrede B. Learning How to Speak: Imitation-Based Refinement of Syllable Production in an Articulatory-Acoustic Model. Presented at the Forth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Genoa, Italy.This paper proposes an efficient neural network model for learning the articulatory-acoustic forward and inverse mapping of consonant-vowel sequences including coarticulation effects. It is shown that the learned models can generalize vowels as well as consonants to other contexts and that the need for supervised training examples can be reduced by refining initial forward and inverse models using acoustic examples only. The models are initially trained on smaller sets of examples and then improved by presenting auditory goals that are imitated. The acoustic outcomes of the imitations together with the executed actions provide new training pairs. It is shown that this unsupervised and imitation-based refinement significantly decreases the error of the forward as well as the inverse model. Using a state-of-the-art articulatory speech synthesizer, our approach allows to reproduce the acoustics from learned articulatory trajectories, i.e. we can listen to the results and rate their quality by error measures and perception

Efficient Bootstrapping of Vocalization Skills Using Active Goal Babbling

Philippsen A, Reinhart F, Wrede B. Efficient Bootstrapping of Vocalization Skills Using Active Goal Babbling. Presented at the International Workshop on Speech Robotics at Interspeech 2015, Dresden, Germany.We use goal babbling, a recent approach to bootstrapping inverse models, for vowel acquisition. In contrast to motor babbling, goal babbling organizes exploration in a low-dimensional goal space. While such a goal space is naturally given in many motor learning tasks, the difficulty in modeling speech production lies within the complexity of acoustic features. Often, the first and second formants are used as low-dimensional features. However, formants cannot capture richer characteristics of acoustic signals.We propose to use high-dimensional acoustic features based on a cochlea model and apply dimension reduction in order to generate a low-dimensional goal space. Instead of pre-defining targets in this goal space, we estimate a target distribution from ambient speech with a Gaussian Mixture Model. We demonstrate that goal babbling can be successfully applied in this goal space in order to learn a parametric model of vowel production specialized to a set of ambient speech sounds. By augmenting the goal-directed exploration along linear paths with an active selection of targets, we achieve a significant speed up in learning

Goal Babbling of Acoustic-Articulatory Models with Adaptive Exploration Noise

Philippsen A, Reinhart F, Wrede B. Goal Babbling of Acoustic-Articulatory Models with Adaptive Exploration Noise. Presented at the Sixth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Cergy-Pontoise / Paris, France

Design and Implementation of Intelligent Control Software for a Dough Kneader

Oestersötebier F, Traphöner P, Reinhart F, Wessels S, Trächtler A. Design and Implementation of Intelligent Control Software for a Dough Kneader. In: International Conference on System-integrated Intelligence. In Press

Impact of Regularization on the Model Space for Time Series Classification

Aswolinskiy W, Reinhart F, Steil JJ. Impact of Regularization on the Model Space for Time Series Classification. In: New Challenges in Neural Computation (NC2). 2015: 49-56