Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

On the development of evolutionary artificial artists

The creation and the evaluation of aesthetic artifacts are tasks related to design, music and art, which are highly interesting from the computational point of view. Nowadays, Artificial Intelligence systems face the challenge of performing tasks that are typically human, highly subjective, and eventually social. The present paper introduces an architecture which is capable of evaluating aesthetic characteristics of artifacts and of creating artifacts that obey certain aesthetic properties. The development methodology and motivation, as well as the results achieved by the various components of the architecture, are described. The potential contributions of this type of systems in the context of digital art are also considered.http://www.sciencedirect.com/science/article/B6TYG-4PTMXVB-1/1/265a0f6c8e478822e6de32b87bc2fb1

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application

Transcranial magnetic stimulation procedures use a magnetic field to carry a short-lasting electrical current pulse into the brain, where it stimulates neurons, particularly in superficial regions of the cerebral cortex. It is a powerfull tool to calculate several parameters related to the intracortical excitability and inhibition of the motor cortex. The cortical silent period (CSP), evoked by magnetic stimulation, corresponds to the suppression of muscle activity for a short period after a muscle response to a magnetic stimulation. The duration of the CSP is paramount to assess intracortical inhibition, and it is known to be correlated with the prognosis of stroke patients’ motor ability. Current mechanisms to estimate the duration of the CSP are mostly based on the analysis of raw electromyographical (EMG) signal and they are very sensitive to the presence of noise. This master thesis is devoted to the analysis of the EMG signal of stroke patients under rehabilitation. The use of advanced statistical machine learning techniques that behave robustly in the presence of noise for this analysis allows us to accurately estimate signal parameters such as the CSP. The research reported in this thesis provides us with a first evidence about their applicability in other areas of neuroscience

Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application

Transcranial magnetic stimulation procedures use a magnetic field to carry a short-lasting electrical current pulse into the brain, where it stimulates neurons, particularly in superficial regions of the cerebral cortex. It is a powerfull tool to calculate several parameters related to the intracortical excitability and inhibition of the motor cortex. The cortical silent period (CSP), evoked by magnetic stimulation, corresponds to the suppression of muscle activity for a short period after a muscle response to a magnetic stimulation. The duration of the CSP is paramount to assess intracortical inhibition, and it is known to be correlated with the prognosis of stroke patients’ motor ability. Current mechanisms to estimate the duration of the CSP are mostly based on the analysis of raw electromyographical (EMG) signal and they are very sensitive to the presence of noise. This master thesis is devoted to the analysis of the EMG signal of stroke patients under rehabilitation. The use of advanced statistical machine learning techniques that behave robustly in the presence of noise for this analysis allows us to accurately estimate signal parameters such as the CSP. The research reported in this thesis provides us with a first evidence about their applicability in other areas of neuroscience

Music Learning with Long Short Term Memory Networks

Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long short-term memory (LSTM) artificial neural networks have been shown to be efficient in sequence learning tasks thanks to their inherent ability to bridge long time lags between input events and their target signals. Here, I investigate the possibility of training LSTM networks to learn and reproduce musical sequences and eventually better understand some of the mechanisms neural networks deploy to learn and compose long time scale structures. To be able to learn music with LSTM networks requires representing musical sequences in these networks. The musical representation developed for this work is inspired by the tonotopic representation of sounds in the auditory system. It is shown that LSTM networks are able to learn each note transitions of the monophonic and polyphonic versions of a simple song using a particular network architecture where both input and output of LSTM networks are musical notes in the developed network representation. However, this architecture for LSTM networks fail to learn longer and more complex musical sequences (e.g. the J.S. Bach cello suites). To solve this problem, I introduce the separation of time scales model, which consists in two connected LSTM networks, operating on different time scales. On one hand, trained slow time scale LSTM networks produce transitions between unique identifiers of musical patterns, which resemble a compressed memory of the pattern akin to neural memory. This gives the long time structure of music. On the other hand, trained fast time scale LSTM networks are producing the note-to-note transitions of each musical patterns. The latter receives as additional inputs the identifiers from slow time scale networks, akin to feed- forward input from memory regions of the brain. These unique identifiers bias fast time scale LSTM networks toward the production of the corresponding musical pattern. The most efficient identifiers of musical patterns are found to be a representation of how similar patterns are from one another. Finally, when unlearned pattern identifiers are given to trained fast time scale networks, novel musical patterns are created from the learned production rules. I show that the introduction of a separation of time scales greatly improves the capacity of LSTM networks to learn a larger body of musical sequences. Finally, I demonstrate that previously unseen input biases can be used to induce the network into the generation of new musical sequences, akin but not similar to known patterns. This presents a possible first step towards the generalization of previously learnt musical knowledge to the creation and composition of new music by artificial neural networks