Fachbereich Informatik - Aktuell

03.09.2018

Disputation Albert Mukovskiy

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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