Dynamically stepping over large obstacle utilizing PSO optimization in the B4LC system

This paper proposes a control structure to resolve the issue of dynamically stepping over large obstacles in the B4LC control system. We reform the local control units LegSwing, LockHip and KneeF lexion respectively. The optimization module with Particle Swarm Optimization (PSO) method is employed to tune the parameters of those controllers by formulating locomotion stability. The optimization process and further validation are conducted on a 3-dimensional simulated bipedal robot. The simulation results reveal that the suggested approach enables robot to dynamically step over a large obstacle with 20cm height by 15cm width in a short time duration

Exploring Kinodynamic Fabrics for Reactive Whole-Body Control of Underactuated Humanoid Robots

For bipedal humanoid robots to successfully operate in the real world, they must be competent at simultaneously executing multiple motion tasks while reacting to unforeseen external disturbances in real-time. We propose Kinodynamic Fabrics as an approach for the specification, solution and simultaneous execution of multiple motion tasks in real-time while being reactive to dynamism in the environment. Kinodynamic Fabrics allows for the specification of prioritized motion tasks as forced spectral semi-sprays and solves for desired robot joint accelerations at real-time frequencies. We evaluate the capabilities of Kinodynamic fabrics on diverse physically challenging whole-body control tasks with a bipedal humanoid robot both in simulation and in the real-world. Kinodynamic Fabrics outperforms the state-of-the-art Quadratic Program based whole-body controller on a variety of whole-body control tasks on run-time and reactivity metrics in our experiments. Our open-source implementation of Kinodynamic Fabrics as well as robot demonstration videos can be found at this url: https://adubredu.github.io/kinofabs

A generic optimization-based framework for reactive collision avoidance in bipedal locomotion

In this work we present a novel and generic framework for reactive collision avoidance in bipedal locomotion, which is formulated as an optimization problem considering the constraints of collision avoidance as well as others (e.g. joint limits) to simultaneously satisfy both Cartesian and joint space objectives. To realize the reactive behaviors, several task space motions, such as the translational motion of the swing foot and the vertical position of the support foot, could be relaxed in presence of obstacles. Therefore, the swing foot trajectory is modulated with respect to the references in real-time for preventing future collisions between the legs, or legs and obstacles in the environment. External obstacle negotiation in the proposed framework can also be addressed generically by treating the obstacle as an extended segment of the support foot. The allowable deviation of the relaxed degrees of freedom from their references could be further utilized to modify the foot placement to regenerate a reactive walking pattern. The validation and the performance of the proposed method are fully evaluated and demonstrated in physics based simulations of the compliant humanoid robot COMAN

Motion Planning and Feedback Control of Simulated Robots in Multi-Contact Scenarios

Diese Dissertation präsentiert eine optimale steuerungsbasierte Architektur für die Bewegungsplanung und Rückkopplungssteuerung simulierter Roboter in Multikontaktszenarien. Bewegungsplanung und -steuerung sind grundlegende Bausteine für die Erstellung wirklich autonomer Roboter. Während in diesen Bereichen enorme Fortschritte für Manipulatoren mit festem Sockel und Radrobotern in den letzten Jahren erzielt wurden, besteht das Problem der Bewegungsplanung und -steuerung für Roboter mit Armen und Beinen immer noch ein ungelöstes Problem, das die Notwendigkeit effizienterer und robusterer Algorithmen belegt. In diesem Zusammenhang wird in dieser Dissertation eine Architektur vorgeschlagen, mit der zwei Hauptherausforderungen angegangen werden sollen, nämlich die effiziente Planung von Kontaktsequenzen und Ganzkörperbewegungen für Floating-Base-Roboter sowie deren erfolgreiche Ausführung mit Rückkopplungsregelungsstrategien, die Umgebungsunsicherheiten bewältigen könne

Kontextsensitive Körperregulierung für redundante Roboter

In the past few decades the classical 6 degrees of freedom manipulators' dominance has been challenged by the rise of 7 degrees of freedom redundant robots. Similarly, with increased availability of humanoid robots in academic research, roboticists suddenly have access to highly dexterous platforms with multiple kinematic chains capable of undertaking multiple tasks simultaneously. The execution of lower-priority tasks, however, are often done in task/scenario specific fashion. Consequently, these systems are not scalable and slight changes in the application often implies re-engineering the entire control system and deployment which impedes the development process over time. This thesis introduces an alternative systematic method of addressing the secondary tasks and redundancy resolution called, context aware body regulation. Contexts consist of one or multiple tasks, however, unlike the conventional definitions, the tasks within a context are not rigidly defined and maintain some level of abstraction. For instance, following a particular trajectory constitutes a concrete task while performing a Cartesian motion with the end-effector represents an abstraction of the same task and is more appropriate for context formulation. Furthermore, contexts are often made up of multiple abstract tasks that collectively describe a reoccurring situation. Body regulation is an umbrella term for a collection of schemes for addressing the robots' redundancy when a particular context occurs. Context aware body regulation offers several advantages over traditional methods. Most notably among them are reusability, scalability and composability of contexts and body regulation schemes. These three fundamental concerns are realized theoretically by in-depth study and through mathematical analysis of contexts and regulation strategies; and are practically implemented by a component based software architecture that complements the theoretical aspects. The findings of the thesis are applicable to any redundant manipulator and humanoids, and allow them to be used in real world applications. Proposed methodology presents an alternative approach for the control of robots and offers a new perspective for future deployment of robotic solutions.Im Verlauf der letzten Jahrzehnte wich der Einfluss klassischer Roboterarme mit 6 Freiheitsgraden zunehmend denen neuer und vielfältigerer Manipulatoren mit 7 Gelenken. Ebenso stehen der Forschung mit den neuartigen Humanoiden inzwischen auch hoch-redundante Roboterplattformen mit mehreren kinematischen Ketten zur Verfügung. Diese überaus flexiblen und komplexen Roboter-Kinematiken ermöglichen generell das gleichzeitige Verfolgen mehrerer priorisierter Bewegungsaufgaben. Die Steuerung der weniger wichtigen Aufgaben erfolgt jedoch oft in anwendungsspezifischer Art und Weise, welche die Skalierung der Regelung zu generellen Kontexten verhindert. Selbst kleine Änderungen in der Anwendung bewirken oft schon, dass große Teile der Robotersteuerung überarbeitet werden müssen, was wiederum den gesamten Entwicklungsprozess behindert. Diese Dissertation stellt eine alternative, systematische Methode vor um die Redundanz neuer komplexer Robotersysteme zu bewältigen und vielfältige, priorisierte Bewegungsaufgaben parallel zu steuern: Die so genannte kontextsensitive Körperregulierung. Darin bestehen Kontexte aus einer oder mehreren Bewegungsaufgaben. Anders als in konventionellen Anwendungen sind die Aufgaben nicht fest definiert und beinhalten eine gewisse Abstraktion. Beispielsweise stellt das Folgen einer bestimmten Trajektorie eine sehr konkrete Bewegungsaufgabe dar, während die Ausführung einer Kartesischen Bewegung mit dem Endeffektor eine Abstraktion darstellt, die für die Kontextformulierung besser geeignet ist. Kontexte setzen sich oft aus mehreren solcher abstrakten Aufgaben zusammen und beschreiben kollektiv eine sich wiederholende Situation. Durch die Verwendung der kontextsensitiven Körperregulierung ergeben sich vielfältige Vorteile gegenüber traditionellen Methoden: Wiederverwendbarkeit, Skalierbarkeit, sowie Komponierbarkeit von Konzepten. Diese drei fundamentalen Eigenschaften werden in der vorliegenden Arbeit theoretisch mittels gründlicher mathematischer Analyse aufgezeigt und praktisch mittels einer auf Komponenten basierenden Softwarearchitektur realisiert. Die Ergebnisse dieser Dissertation lassen sich auf beliebige redundante Manipulatoren oder humanoide Roboter anwenden und befähigen diese damit zur realen Anwendung außerhalb des Labors. Die hier vorgestellte Methode zur Regelung von Robotern stellt damit eine neue Perspektive für die zukünftige Entwicklung von robotischen Lösungen dar

An adaptive compliance Hierarchical Quadratic Programming controller for ergonomic human–robot collaboration

This paper proposes a novel Augmented Hierarchical Quadratic Programming (AHQP) framework for multi-tasking control in Human-Robot Collaboration (HRC) which integrates human-related parameters to optimize ergonomics. The aim is to combine parameters that are typical of both industrial applications (e.g. cycle times, productivity) and human comfort (e.g. ergonomics, preference), to identify an optimal trade-off. The augmentation aspect avoids the dependency from a fixed end-effector reference trajectory, which becomes part of the optimization variables and can be used to define a feasible workspace region in which physical interaction can occur. We then demonstrate that the integration of the proposed AHQP in HRC permits the addition of human ergonomics and preference. To achieve this, we develop a human ergonomics function based on the mapping of an ergonomics score, compatible with AHQP formulation. This allows to identify at control level the optimal Cartesian pose that satisfies the active objectives and constraints, that are now linked to human ergonomics. In addition, we build an adaptive compliance framework that integrates both aspects of human preferences and intentions, which are finally tested in several collaborative experiments using the redundant MOCA robot. Overall, we achieve improved human ergonomics and health conditions, aiming at the potential reduction of work-related musculoskeletal disorders

Ein hierarchisches Framework für physikalische Mensch-Roboter-Interaktion

Robots are becoming more than machines performing repetitive tasks behind safety fences, and are expected to perform multiple complex tasks and work together with a human. For that purpose, modern robots are commonly built with two main characteristics: a large number of joints to increase their versatility and the capability to feel the environment through torque/force sensors. Controlling such complex robots requires the development of sophisticated frameworks capable of handling multiple tasks. Various frameworks have been proposed in the last years to deal with the redundancy caused by a large number of joints. Those hierarchical frameworks prioritize the achievement of the main task with the whole robot capability, while secondary tasks are performed as well as the remaining mobility allows it. This methodology presents considerable drawbacks in applications requiring that the robot respects constraints imposed by, e.g., safety restrictions or physical limitations. One particular case is unilateral constraints imposed by, e.g., joint or workspace limits. To implement them, task hierarchical frameworks rely on the activation of repulsive potential fields when approaching the limit. The performance of the potential field depends on the configuration and speed of the robot. Additionally, speed limitation is commonly required in collaborative scenarios, but it has been insufficiently treated for torque-controlled robots. With the aim of controlling redundant robots in collaborative scenarios, this thesis proposes a framework that handles multiple tasks under multiple constraints. The robot’s reaction to physical interaction must be intuitive and safe for humans: The robot must not impose high forces on the human or react unexpectedly to external forces. The proposed framework uses novel methods to avoid exceeding position, velocity and acceleration limits in joint and Cartesian spaces. A comparative study is carried out between different redundancy resolution solvers to contrast the diverse approaches used to solve the redundancy problem. Widely used projector-based and quadratic programming-based hierarchical solvers were experimentally analyzed when reacting to external forces. Experiments were performed using an industrial redundant collaborative robot. Results demonstrate that the proposed method to handle unilateral constraints produces a safe and expected reaction in the presence of external forces exerted by humans. The robot does not exceed the given limits, while the tasks performed are prioritized hierarchically

Toward Robots with Peripersonal Space Representation for Adaptive Behaviors

The abilities to adapt and act autonomously in an unstructured and human-oriented environment are necessarily vital for the next generation of robots, which aim to safely cooperate with humans. While this adaptability is natural and feasible for humans, it is still very complex and challenging for robots. Observations and findings from psychology and neuroscience in respect to the development of the human sensorimotor system can inform the development of novel approaches to adaptive robotics. Among these is the formation of the representation of space closely surrounding the body, the Peripersonal Space (PPS) , from multisensory sources like vision, hearing, touch and proprioception, which helps to facilitate human activities within their surroundings. Taking inspiration from the virtual safety margin formed by the PPS representation in humans, this thesis first constructs an equivalent model of the safety zone for each body part of the iCub humanoid robot. This PPS layer serves as a distributed collision predictor, which translates visually detected objects approaching a robot\u2019s body parts (e.g., arm, hand) into the probabilities of a collision between those objects and body parts. This leads to adaptive avoidance behaviors in the robot via an optimization-based reactive controller. Notably, this visual reactive control pipeline can also seamlessly incorporate tactile input to guarantee safety in both pre- and post-collision phases in physical Human-Robot Interaction (pHRI). Concurrently, the controller is also able to take into account multiple targets (of manipulation reaching tasks) generated by a multiple Cartesian point planner. All components, namely the PPS, the multi-target motion planner (for manipulation reaching tasks), the reaching-with-avoidance controller and the humancentred visual perception, are combined harmoniously to form a hybrid control framework designed to provide safety for robots\u2019 interactions in a cluttered environment shared with human partners. Later, motivated by the development of manipulation skills in infants, in which the multisensory integration is thought to play an important role, a learning framework is proposed to allow a robot to learn the processes of forming sensory representations, namely visuomotor and visuotactile, from their own motor activities in the environment. Both multisensory integration models are constructed with Deep Neural Networks (DNNs) in such a way that their outputs are represented in motor space to facilitate the robot\u2019s subsequent actions

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