Fachbereich Informatik - Aktuell
03.09.2018
Disputation Albert Mukovskiy
am Mittwoch, 12. September 2018, um 10:00 Uhr im Hertie-Institut für klinische Hirnforschung, Otfried-Müller-Str. 27, Kliniken Berg, Raum 2.310
Albert Mukovskiy
Computational Methods for Cognitive and Cooperative Robotics
Berichterstatter 1: Prof. Dr. Martin Giese
Berichterstatter 2: Prof. Dr. Michael Huber
In the last decades design methods in control engineering made substantial progress in
the areas of robotics and computer animation. Nowadays these methods incorporate the
newest developments in machine learning and artificial intelligence. But the problems
of flexible and online-adaptive combinations of motor behaviors remain challenging for
human-like animations and for humanoid robotics. In this context, biologically-motivated
methods for the analysis and re-synthesis of human motor programs provide new insights
in and models for the anticipatory motion synthesis.
This thesis presents the author’s achievements in the areas of cognitive and developmental
robotics, cooperative and humanoid robotics and intelligent and machine learning
methods in computer graphics. The first part of the thesis in the chapter “Goal-directed
Imitation for Robots” considers imitation learning in cognitive and developmental robotics.
The work presented here details the author’s progress in the development of hierarchical
motion recognition and planning inspired by recent discoveries of the functions of mirrorneuron
cortical circuits in primates. The overall architecture is capable of ‘learning for
imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time
capable path planning subsystem for obstacle avoidance during arm reaching. The learningbased
path planning subsystem is universal for all types of anthropomorphic robot arms,
and is capable of knowledge transfer at the level of individual motor acts.
Next, the problems of learning and synthesis of motor synergies, the spatial and spatiotemporal
combinations of motor features in sequential multi-action behavior, and the
problems of task-related action transitions are considered in the second part of the thesis
“Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new
approach of modeling complex full-body human actions by mixtures of time-shift invariant
motor primitives in presented. The online-capable full-body motion generation architecture
based on dynamic movement primitives driving the time-shift invariant motor synergies
was implemented as an online-reactive adaptive motion synthesis for computer graphics
and robotics applications.
The last part of the thesis entitled “Contraction Theory and Self-organized Scenarios
in Computer Graphics and Robotics” is dedicated to optimal control strategies in multiiii
agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last
part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.
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