Computational Methods for Cognitive and Cooperative Robotics

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 mirror-neuron 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 learning-based 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 spatio-temporal 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 chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-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.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der künstlichen Intelligenz. Die flexible und echtzeitfähige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung für die Generierung menschenähnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle für die antizipatorische Bewegungssynthese. Diese Dissertation präsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung für Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden für die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthält ein echtzeitfähiges Pfadplanungssubsystem zur Hindernisvermeidung während der Durchführung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und für alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu übertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese für Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von räumlichen und räumlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs übergängen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fähiger Synthesealgorithmus für Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde für verschiedene Probleme der Bewegungssynthese für die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil präsentiert neue mathematische Werkzeuge für die Stabilitätsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

On the development of intention understanding for joint action tasks

Our everyday, common sense ability to discern the intentions of others’ from their motions is fundamental for a successful cooperation in joint action tasks. In this paper we address in a modeling study the question of how the ability to understand complex goal-directed action sequences may develop during learning and practice. The model architecture reflects recent neurophysiological findings that suggest the existence of chains of mirror neurons associated with specific goals. These chains may be activated by external events to simulate the consequences of observed actions. Using the mathematical framework of dynamical neural fields to model the dynamics of different neural populations representing goals, action means and contextual cues, we show that such chains may develop based on a local, Hebbian learning rule. We validate the functionality of the learned model in a joint action task in which an observer robot infers the intention of a partner to chose a complementary action sequence.Fundação para a Ciência e a Tecnologia (FCT)European Commission (EC

Modeling of predictive human movement coordination patterns for applications in computer graphics

The planning of human body movements is highly predictive. Within a sequence of actions, the anticipation of a final task goal modulates the individual actions within the overall pattern of motion. An example is a sequence of steps, which is coordinated with the grasping of an object at the end of the step sequence. Opposed to this property of natural human movements, real-time animation systems in computer graphics often model complex activities by a sequential concatenation of individual pre-stored movements, where only the movement before accomplishing the goal is adapted. We present a learning-based technique that models the highly adaptive predictive movement coordination in humans, illustrated for the example of the coordination of walking and reaching. The proposed system for the real-time synthesis of human movements models complex activities by a sequential concatenation of movements, which are approximated by the superposition of kinematic primitives that have been learned from trajectory data by anechoic demixing, using a step-wise regression approach. The kinematic primitives are then approximated by stable solutions of nonlinear dynamical systems (dynamic primitives) that can be embedded in control architectures. We present a control architecture that generates highly adaptive predictive full-body movements for reaching while walking with highly human-like appearance. We demonstrate that the generated behavior is highly robust, even in presence of strong perturbations that require the insertion of additional steps online in order to accomplish the desired task

Neural circuits underlying action understanding

Comunicação apresentada na "Joint-Action Science and Technology (JAST) Opening Conference", em Atenas, Grécia, em 2005.European Grant JAST (IST-2-003747-IP)

Action-understanding and imitation learning in a robot-human task

We report results of an interdisciplinary project which aims at endowing a real robot system with the capacity for learning by goaldirected imitation. The control architecture is biologically inspired as it reflects recent experimental findings in action observation/execution studies. We test its functionality in variations of an imitation paradigm in which the artefact has to reproduce the observed or inferred end state of a grasping-placing sequence displayed by a human model.European grantArteSImit (IST-2000-29686)

A dynamic model for action understanding and goal-directed imitation

The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.European Commission JAST project IST-2-003747-I

Analysis and design of the dynamical stability of collective behavior in crowds

The modeling of the dynamics of the collective behavior of multiple characters is a key problem in crowd animation. Collective behavior can be described by the solutions of large-scale nonlinear dynamical systems that describe the dynamical interaction of locomoting characters with highly nonlinear articulation dynamics. The design of the stability properties of such complex multi-component systems has been rarely studied in computer animation. We present an approach for the solution of this problem that is based on Contraction Theory, a novel framework for the analysis of the stability complex nonlinear dynamical systems. Using a learning-based realtime-capable architecture for the animation of crowds, we demonstrate the application of this novel approach for the stability design for the groups of characters that interact in various ways. The underlying dynamics specifies control rules for propagation speed and direction, and for the synchronization of the gait phases. Contraction theory is not only suitable for the derivation of conditions that guarantee global asymptotic stability, but also of minimal convergence rates. Such bounds permit to guarantee the temporal constraints for the order formation in self-organizing interactive crowds. Keywords: computer animation, crowd animation, coordination, distributed control, stabilityDeutsche ForschungsgemeinschaftEuropean Commission (Grant 248311)Hermann and Lilly Schilling Foundation for Medical Researc

Synthesis of Character Behaviour by Dynamic Interaction of Synergies Learned from Motion Capture Data

Character animation ideally combines the competing requirements of high realism and flexible automatic generation of behaviour. A method for real-time human character animation is presented, which self-organizes character behaviour with high degrees of realism by dynamic coupling of ’synergies’ that are learned from motion capture data. Based on a new algorithm for blind source separation that considers time delays, highly compact generative models of body movements are learned from motion capture data. The learned components are mapped onto stable solutions of dynamical systems applying kernel methods, resulting in a coupled network of dynamic pattern generators whose state can be updated in real-time. This new framework is applied for crowd animation and the automatic generation of interactive behaviour for multiple characters