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2019


Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems
Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

ics

PDF [BibTex]

2019


PDF [BibTex]


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Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL University, 2019 (mastersthesis)

Abstract
Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

ics

[BibTex]

[BibTex]

2017


Robotic Motion Learning Framework to Promote Social Engagement
Robotic Motion Learning Framework to Promote Social Engagement

Burns, R.

The George Washington University, August 2017 (mastersthesis)

Abstract
This paper discusses a novel framework designed to increase human-robot interaction through robotic imitation of the user's gestures. The set up consists of a humanoid robotic agent that socializes with and play games with the user. For the experimental group, the robot also imitates one of the user's novel gestures during a play session. We hypothesize that the robot's use of imitation will increase the user's openness towards engaging with the robot. Preliminary results from a pilot study of 12 subjects are promising in that post-imitation, experimental subjects displayed a more positive emotional state, had higher instances of mood contagion towards the robot, and interpreted the robot to have a higher level of autonomy than their control group counterparts. These results point to an increased user interest in engagement fueled by personalized imitation during interaction.

hi

link (url) [BibTex]

2017


link (url) [BibTex]

2015


Gaussian Process Optimization for Self-Tuning Control
Gaussian Process Optimization for Self-Tuning Control

Marco, A.

Polytechnic University of Catalonia (BarcelonaTech), October 2015 (mastersthesis)

am ics

PDF Project Page [BibTex]

2015


PDF Project Page [BibTex]


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Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

am ics

[BibTex]

[BibTex]


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Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

am ics

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]

2014


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Opto-thermal micro-transportation for cellular microbiology

Hu, W.

University of Hawai’i at Manoa, 2014 (phdthesis)

pi

[BibTex]

2014


[BibTex]

2013


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Determination of an Analysis Procedure for FEM-Based Fatigue Calculations

Serhat, G.

Technical University of Munich, December 2013 (mastersthesis)

hi

[BibTex]

2013


[BibTex]