Decision shaping and strategy learning in multi-robot interactions

Recent developments in robot technology have contributed to the advancement of autonomous behaviours in human-robot systems; for example, in following instructions received from an interacting human partner. Nevertheless, increasingly many systems are moving towards more seamless forms of interaction, where factors such as implicit trust and persuasion between humans and robots are brought to the fore. In this context, the problem of attaining, through suitable computational models and algorithms, more complex strategic behaviours that can influence human decisions and actions during an interaction, remains largely open. To address this issue, this thesis introduces the problem of decision shaping in strategic interactions between humans and robots, where a robot seeks to lead, without however forcing, an interacting human partner to a particular state. Our approach to this problem is based on a combination of statistical modeling and synthesis of demonstrated behaviours, which enables robots to efficiently adapt to novel interacting agents. We primarily focus on interactions between autonomous and teleoperated (i.e. human-controlled) NAO humanoid robots, using the adversarial soccer penalty shooting game as an illustrative example. We begin by describing the various challenges that a robot operating in such complex interactive environments is likely to face. Then, we introduce a procedure through which composable strategy templates can be learned from provided human demonstrations of interactive behaviours. We subsequently present our primary contribution to the shaping problem, a Bayesian learning framework that empirically models and predicts the responses of an interacting agent, and computes action strategies that are likely to influence that agent towards a desired goal. We then address the related issue of factors affecting human decisions in these interactive strategic environments, such as the availability of perceptual information for the human operator. Finally, we describe an information processing algorithm, based on the Orient motion capture platform, which serves to facilitate direct (as opposed to teleoperation-mediated) strategic interactions between humans and robots. Our experiments introduce and evaluate a wide range of novel autonomous behaviours, where robots are shown to (learn to) influence a variety of interacting agents, ranging from other simple autonomous agents, to robots controlled by experienced human subjects. These results demonstrate the benefits of strategic reasoning in human-robot interaction, and constitute an important step towards realistic, practical applications, where robots are expected to be not just passive agents, but active, influencing participants

Modular and Safe Event-Driven Programming

Asynchronous event-driven systems are ubiquitous across domains such as device drivers, distributed systems, and robotics. These systems are notoriously hard to get right as the programmer needs to reason about numerous control paths resulting from the complex interleaving of events (or messages) and failures. Unsurprisingly, it is easy to introduce subtle errors while attempting to fill in gaps between high-level system specifications and their concrete implementations.This dissertation proposes new methods for programming safe event-driven asynchronous systems.In the first part of the thesis, we present ModP, a modular programming framework for compositional programming and testing of event-driven asynchronous systems.The ModP module system supports a novel theory of compositional refinement for assume-guarantee reasoning of dynamic event-driven asynchronous systems. We build a complex distributed systems software stack using ModP.Our results demonstrate that compositional reasoning can help scale model-checking (both explicit and symbolic) to large distributed systems.ModP is transforming the way asynchronous software is built at Microsoft and Amazon Web Services (AWS). Microsoft uses ModP for implementing safe device drivers and other software in the Windows kernel.AWS uses ModP for compositional model checking of complex distributed systems. While ModP simplifies analysis of such systems, the state space of industrial-scale systems remains extremely large.In the second part of this thesis, we present scalable verification and systematic testing approaches to further mitigate this state-space explosion problem.First, we introduce the concept of a delaying explorer to perform prioritized exploration of the behaviors of an asynchronous reactive program. A delaying explorer stratifies the search space using a custom strategy (tailored towards finding bugs faster), and a delay operation that allows deviation from that strategy. We show that prioritized search with a delaying explorer performs significantly better than existing approaches for finding bugs in asynchronous programs.Next, we consider the challenge of verifying time-synchronized systems; these are almost-synchronous systems as they are neither completely asynchronous nor synchronous.We introduce approximate synchrony, a sound and tunable abstraction for verification of almost-synchronous systems. We show how approximate synchrony can be used for verification of both time-synchronization protocols and applications running on top of them.Moreover, we show how approximate synchrony also provides a useful strategy to guide state-space exploration during model-checking.Using approximate synchrony and implementing it as a delaying explorer, we were able to verify the correctness of the IEEE 1588 distributed time-synchronization protocol and, in the process, uncovered a bug in the protocol that was well appreciated by the standards committee.In the final part of this thesis, we consider the challenge of programming a special class of event-driven asynchronous systems -- safe autonomous robotics systems.Our approach towards achieving assured autonomy for robotics systems consists of two parts: (1) a high-level programming language for implementing and validating the reactive robotics software stack; and (2) an integrated runtime assurance system to ensure that the assumptions used during design-time validation of the high-level software hold at runtime.Combining high-level programming language and model-checking with runtime assurance helps us bridge the gap between design-time software validation that makes assumptions about the untrusted components (e.g., low-level controllers), and the physical world, and the actual execution of the software on a real robotic platform in the physical world. We implemented our approach as DRONA, a programming framework for building safe robotics systems.We used DRONA for building a distributed mobile robotics system and deployed it on real drone platforms. Our results demonstrate that DRONA (with the runtime-assurance capabilities) enables programmers to build an autonomous robotics software stack with formal safety guarantees.To summarize, this thesis contributes new theory and tools to the areas of programming languages, verification, systematic testing, and runtime assurance for programming safe asynchronous event-driven across the domains of fault-tolerant distributed systems and safe autonomous robotics systems

Scaling Robot Motion Planning to Multi-core Processors and the Cloud

Imagine a world in which robots safely interoperate with humans, gracefully and efficiently accomplishing everyday tasks. The robot's motions for these tasks, constrained by the design of the robot and task at hand, must avoid collisions with obstacles. Unfortunately, planning a constrained obstacle-free motion for a robot is computationally complex---often resulting in slow computation of inefficient motions. The methods in this dissertation speed up this motion plan computation with new algorithms and data structures that leverage readily available parallel processing, whether that processing power is on the robot or in the cloud, enabling robots to operate safer, more gracefully, and with improved efficiency. The contributions of this dissertation that enable faster motion planning are novel parallel lock-free algorithms, fast and concurrent nearest neighbor searching data structures, cache-aware operation, and split robot-cloud computation. Parallel lock-free algorithms avoid contention over shared data structures, resulting in empirical speedup proportional to the number of CPU cores working on the problem. Fast nearest neighbor data structures speed up searching in SO(3) and SE(3) metric spaces, which are needed for rigid body motion planning. Concurrent nearest neighbor data structures improve searching performance on metric spaces common to robot motion planning problems, while providing asymptotic wait-free concurrent operation. Cache-aware operation avoids long memory access times, allowing the algorithm to exhibit superlinear speedup. Split robot-cloud computation enables robots with low-power CPUs to react to changing environments by having the robot compute reactive paths in real-time from a set of motion plan options generated in a computationally intensive cloud-based algorithm. We demonstrate the scalability and effectiveness of our contributions in solving motion planning problems both in simulation and on physical robots of varying design and complexity. Problems include finding a solution to a complex motion planning problem, pre-computing motion plans that converge towards the optimal, and reactive interaction with dynamic environments. Robots include 2D holonomic robots, 3D rigid-body robots, a self-driving 1/10 scale car, articulated robot arms with and without mobile bases, and a small humanoid robot.Doctor of Philosoph

Nachweislich sichere Bewegungsplanung für autonome Fahrzeuge durch Echtzeitverifikation

This thesis introduces fail-safe motion planning as the first approach to guarantee legal safety of autonomous vehicles in arbitrary traffic situations. The proposed safety layer verifies whether intended trajectories comply with legal safety and provides fail-safe trajectories when intended trajectories result in safety-critical situations. The presented results indicate that the use of fail-safe motion planning can drastically reduce the number of traffic accidents.Die vorliegende Arbeit führt ein neuartiges Verifikationsverfahren ein, mit dessen Hilfe zum ersten Mal die verkehrsregelkonforme Sicherheit von autonomen Fahrzeugen gewährleistet werden kann. Das Verifikationsverfahren überprüft, ob geplante Trajektorien sicher sind und generiert Rückfalltrajektorien falls diese zu einer unsicheren Situation führen. Die Ergebnisse zeigen, dass die Verwendung des Verfahrens zu einer deutlichen Reduktion von Verkehrsunfällen führt

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