AIICS

Mattias Tiger

Theses

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2022
[3] Mattias Tiger. 2022.
Safety-Aware Autonomous Systems: Preparing Robots for Life in the Real World.
PhD Thesis. In series: Linköping Studies in Science and Technology. Dissertations #2262. Linköping University Electronic Press. 244 pages. ISBN: 9789179295011, 9789179295028.
DOI: 10.3384/9789179295028.
Fulltext: https://doi.org/10.3384/9789179295028
preview image: http://liu.diva-portal.org/smash/get/div...

Real‐world autonomous systems are expected to be increasingly deployed and operating in real‐world environments over the coming decades. Autonomous systems such as AI‐enabled robotic systems and intelligent transportation systems, will alleviate mundane human work, provide new services, and facilitate a smarter and more flexible infrastructure. The real‐ world environments affected include workplaces, public spaces, and homes. To ensure safe operations, in for example the vicinity of people, it is paramount that the autonomous systems are explainable, behave predictable, and can handle that the real world is ever changing and only partially observable. To deal with a dynamic and changing environment, consistently and safely, it is necessary to have sound uncertainty management. Explicit uncertainty quantification is fundamental to providing probabilistic safety guarantees that can also be monitored during runtime to ensure safety in new situations. It is further necessary for well‐grounded prediction and classification uncertainty, for achieving task effectiveness with high robustness and for dealing with unknown unknowns, such as world model divergence, using anomaly detection. This dissertation focuses on the notion of motion in terms of trajectories, from recognizing – to anticipating – to generating – to monitoring that it fulfills expectations such as predictability or other safety constraints during runtime. Efficiency, effectiveness, and safety are competing qualities, and in safety-critical applications the required degree of safety makes it very challenging to reach useful levels of efficiency and effectiveness. To this end, a holistic perspective on agent motion in complex and dynamic environments is investigated. This work leverage synergies in well‐founded formalized interactions and integration between learning, reasoning, and interaction, and demonstrate jointly efficient, effective, and safe capabilities for autonomous systems in safety‐critical situations.

2014
[2] Full text  Mattias Tiger. 2014.
Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework.
Student Thesis. 103 pages. ISRN: LIU-IDA/LITH-EX-A--14/059--SE.

Learning to recognize and predict common activities, performed by objects and observed by sensors, is an important and challenging problem related both to artificial intelligence and robotics.In this thesis, the general problem of dynamic adaptive situation awareness is considered and we argue for the need for an on-line bottom-up approach.A candidate for a bottom layer is proposed, which we consider to be capable of future extensions that can bring us closer towards the goal.We present a novel approach to adaptive activity learning, where a mapping between raw data and primitive activity concepts are learned and continuously improved on-line and unsupervised. The approach takes streams of observations of objects as input and learns a probabilistic representation of both the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using probabilistic graphs.The learned model supports both estimating the most likely activity and predicting the most likely future (and past) activities. Methods and ideas from a wide range of previous work are combined to provide a uniform and efficient way to handle a variety of common problems related to learning, classifying and predicting activities.The framework is evaluated both by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an internally developed autonomous quadcopter system. The conclusion is that our framework is capable of learning the observed activities in real-time with good accuracy.We see this work as a step towards unsupervised learning of activities for robotic systems to adapt to new circumstances autonomously and to learn new activities on the fly that can be detected and predicted immediately.

[1] Full text  Mattias Tiger. 2014.
Sparse Linear Modeling of Speech from EEG.
Student Thesis. 63 pages. ISRN: LiTH-ISY-EX-ET--14/0420--SE.

For people with hearing impairments, attending to a single speaker in a multi-talker background can be very difficult and something which the current hearing aids can barely help with. Recent studies have shown that the audio stream a human focuses on can be found among the surrounding audio streams, using EEG and linear models. With this rises the possibility of using EEG to unconsciously control future hearing aids such that the attuned sounds get enhanced, while the rest are damped. For such hearing aids to be useful for every day usage it better be using something other than a motion sensitive, precisely placed EEG cap. This could possibly be archived by placing the electrodes together with the hearing aid in the ear.One of the leading hearing aid manufacturer Oticon and its research lab Erikholm Research Center have recorded an EEG data set of people listening to sentences and in which electrodes were placed in and closely around the ears. We have analyzed the data set by applying a range of signal processing approaches, mainly in the context of audio estimation from EEG. Two different types of linear sparse models based on L1-regularized least squares are formulated and evaluated, providing automatic dimensionality reduction in that they significantly reduce the number of channels needed. The first model is based on linear combinations of spectrograms and the second is based on linear temporal filtering. We have investigated the usefulness of the in-ear electrodes and found some positive indications. All models explored consider the in-ear electrodes to be the most important, or among the more important, of the 128 electrodes in the EEG cap.This could be a positive indication of the future possibility of using only electrodes in the ears for future hearing aids.