30 research outputs found
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 authorâs 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 âGoal-directed Imitation for Robotsâ considers imitation learning in cognitive and developmental robotics.
The work presented here details the authorâs 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 âlearning for
imitationâ and âlearning by imitationâ. 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
âKinematic Motion Synthesis for Computer Graphics and Roboticsâ. 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 âContraction Theory and Self-organized Scenarios
in Computer Graphics and Roboticsâ 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
kĂŒnstlichen Intelligenz. Die flexible und echtzeitfĂ€hige Kombination von motorischen Verhaltensweisen
ist eine wesentliche Herausforderung fĂŒr die Generierung menschenĂ€hnlicher
Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch
motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme
neue Erkenntnisse und Modelle fĂŒr die antizipatorische Bewegungssynthese.
Diese Dissertation prÀsentiert 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 âZielgerichtete Nachahmung fĂŒr Roboterâ behandelt das Imitationslernen
in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben
neue Methoden fĂŒr 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, âdurch Imitation zu lernenâ
und âzu lernen zu imitierenâ. Das komplette entwickelte System enthĂ€lt ein echtzeitfĂ€higes
Pfadplanungssubsystem zur Hindernisvermeidung wĂ€hrend der DurchfĂŒhrung von Armbewegungen.
Das lernbasierte Pfadplanungssubsystem ist universell und fĂŒr alle Arten von
anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer
Handlungen zu ĂŒbertragen.
Im zweiten Teil der Arbeit âKinematische Bewegungssynthese fĂŒr Computergrafik und
Robotikâ werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h.
von rÀumlichen und rÀumlich-zeitlichen Kombinationen motorischer Bewegungselemente
bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs ĂŒbergĂ€ngen behandelt.
Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen
durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem
wurde ein online-fĂ€higer Synthesealgorithmus fĂŒr Ganzköperbewegungen entwickelt, der
auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten
verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde fĂŒr
verschiedene Probleme der Bewegungssynthese fĂŒr die Computergrafik- und Roboteranwendungen
implementiert.
Das letzte Kapitel der Dissertation mit dem Titel âKontraktionstheorie und selbstorganisierte
Szenarien in der Computergrafik und Robotikâ widmet sich optimalen Kontrollstrategien
in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare
Kinematik gekennzeichnet sind. Dieser letzte Teil prÀsentiert neue mathematische Werkzeuge
fĂŒr die StabilitĂ€tsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien
Scaled Autonomy for Networked Humanoids
Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework.
The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment.
Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC
Leveraging self-supervision for visual embodied navigation with neuralized potential fields
Une tĂąche fondamentale en robotique consiste Ă naviguer entre deux endroits. En particulier, la navigation dans le monde rĂ©el nĂ©cessite une planification Ă long terme Ă l'aide d'images RVB (RGB) en haute dimension, ce qui constitue un dĂ©fi considĂ©rable pour les approches d'apprentissage de bout-en-bout. Les mĂ©thodes semi-paramĂ©triques actuelles parviennent plutĂŽt Ă atteindre des objectifs Ă©loignĂ©s en combinant des modĂšles paramĂ©triques avec une mĂ©moire topologique de l'environnement, souvent reprĂ©sentĂ©e sous forme d'un graphe ayant pour nĆuds des images prĂ©cĂ©demment vues. Cependant, l'utilisation de ces graphes implique gĂ©nĂ©ralement l'ajustement d'heuristiques d'Ă©lagage afin d'Ă©viter les arĂȘtes superflues, limiter la mĂ©moire requise et permettre des recherches raisonnablement rapides dans le graphe.
Dans cet ouvrage, nous montrons comment les approches de bout-en-bout basées sur l'apprentissage auto-supervisé peuvent exceller dans des tùches de navigation à long terme. Nous présentons initialement Duckie-Former (DF), une approche de bout-en-bout pour la navigation visuelle dans des environnements routiers. En utilisant un Vision Transformer (ViT) pré-entraßné avec une méthode auto-supervisée, nous nous inspirons des champs de potentiels afin de dériver une stratégie de navigation utilisant en entrée un masque de segmentation d'image de faible résolution. DF est évalué dans des tùches de navigation de suivi de voie et d'évitement d'obstacles. Nous présentons ensuite notre deuxiÚme approche intitulée One-4-All (O4A). O4A utilise l'apprentissage auto-supervisé et l'apprentissage de variétés afin de créer un pipeline de navigation de bout-en-bout sans graphe permettant de spécifier l'objectif à l'aide d'une image. La navigation est réalisée en minimisant de maniÚre vorace une fonction de potentiel définie de maniÚre continue dans l'espace latent O4A.
Les deux systÚmes sont entraßnés sans interagir avec le simulateur ou le robot sur des séquences d'exploration de données RVB et de contrÎles non experts. Ils ne nécessitent aucune mesure de profondeur ou de pose. L'évaluation est effectuée dans des environnements simulés et réels en utilisant un robot à entraßnement différentiel.A fundamental task in robotics is to navigate between two locations. Particularly, real-world navigation can require long-horizon planning using high-dimensional RGB images, which poses a substantial challenge for end-to-end learning-based approaches. Current semi-parametric methods instead achieve long-horizon navigation by combining learned modules with a topological memory of the environment, often represented as a graph over previously collected images. However, using these graphs in practice typically involves tuning various pruning heuristics to prevent spurious edges, limit runtime memory usage, and allow reasonably fast graph queries.
In this work, we show how end-to-end approaches trained through Self-Supervised Learning (SSL) can excel in long-horizon navigation tasks. We initially present Duckie-Former (DF), an end-to-end approach for visual servoing in road-like environments. Using a Vision Transformer (ViT) pretrained with a self-supervised method, we derive a potential-fields-like navigation strategy based on a coarse image segmentation model. DF is assessed in the navigation tasks of lane-following and obstacle avoidance. Subsequently, we introduce our second approach called One-4-All (O4A). O4A leverages SSL and manifold learning to create a graph-free, end-to-end navigation pipeline whose goal is specified as an image. Navigation is achieved by greedily minimizing a potential function defined continuously over the O4A latent space. O4A is evaluated in complex indoor environments.
Both systems are trained offline on non-expert exploration sequences of RGB data and controls, and do not require any depth or pose measurements. Assessment is performed in simulated and real-world environments using a differential-drive robot
SYCOPHANT WIRELESS SENSOR NETWORKS TRACKED BY SPARSE MOBILE WIRELESS SENSOR NETWORKS WHILE COOPERATIVELY MAPPING AN AREA
Documentos apresentados no Ăąmbito do reconhecimento de graus e diplomas estrangeiro
Motion synthesis for high degree-of-freedom robots in complex and changing environments
The use of robotics has recently seen significant growth in various domains such as
unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid
robots. However, one of the most important and essential capabilities required for
long term autonomy, which is the ability to operate robustly and safely in real-world
environments, in contrast to industrial and laboratory setup is largely missing. Designing
robots that can operate reliably and efficiently in cluttered and changing
environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e.
robots with multiple actuators. On one hand, the dexterity offered by the kinematic
redundancy allows the robot to perform dexterous manipulation tasks in complex
environments, whereas on the other hand, such complex system also makes controlling
and planning very challenging. To address such two interrelated problems, we
exploit robot motion synthesis from three perspectives that feed into each other: end-pose
planning, motion planning and motion adaptation. We propose several novel
ideas in each of the three phases, using which we can efficiently synthesise dexterous
manipulation motion for fixed-base robotic arms, mobile manipulators, as well as
humanoid robots in cluttered and potentially changing environments.
Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite
for other modules such as motion planning, is an important and yet unsolved
problem in robotics. Such information is often assumed given, or manually provided
in practice, which significantly limiting high-level autonomy. In our research, by using
novel data pre-processing and encoding techniques, we are able to efficiently
search for collision-free end-poses in challenging scenarios in the presence of uneven
terrains.
After having found the end-poses, the motion planning module can proceed. Although
motion planning has been claimed as well studied, we find that existing algorithms
are still unreliable for robust and safe operations in real-world applications,
especially when the environment is cluttered and changing. We propose a novel
resolution complete motion planning algorithm, namely the Hierarchical Dynamic
Roadmap, that is able to generate collision-free motion trajectories for redundant
robotic arms in extremely complicated environments where other methods would fail.
While planning for fixed-base robotic arms is relatively less challenging, we also investigate
into efficient motion planning algorithms for high DoF (30 - 40) humanoid
robots, where an extra balance constraint needs to be taken into account. The result
shows that our method is able to efficiently generate collision-free whole-body trajectories
for different humanoid robots in complex environments, where other methods
would require a much longer planning time.
Both end-pose and motion planning algorithms compute solutions in static environments,
and assume the environments stay static during execution. While human
and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To
address this issue, we propose a novel state space representation, the Distance Mesh
space, in which the robot is able to remap the pre-planned motion in real-time and
adapt to environmental changes during execution.
By utilizing the proposed end-pose planning, motion planning and motion adaptation
techniques, we obtain a robotic framework that significantly improves the
level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR
(7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator),
Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile
bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing
that our methods are truly applicable to solve high dimensional motion planning
for practical problems