Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Adaptive control of compliant robots with Reservoir Computing

In modern society, robots are increasingly used to handle dangerous, repetitive and/or heavy tasks with high precision. Because of the nature of the tasks, either being dangerous, high precision or simply repetitive, robots are usually constructed with high torque motors and sturdy materials, that makes them dangerous for humans to handle. In a car-manufacturing company, for example, a large cage is placed around the robot’s workspace that prevents humans from entering its vicinity. In the last few decades, efforts have been made to improve human-robot interaction. Often the movement of robots is characterized as not being smooth and clearly dividable into sub-movements. This makes their movement rather unpredictable for humans. So, there exists an opportunity to improve the motion generation of robots to enhance human-robot interaction. One interesting research direction is that of imitation learning. Here, human motions are recorded and demonstrated to the robot. Although the robot is able to reproduce such movements, it cannot be generalized to other situations. Therefore, a dynamical system approach is proposed where the recorded motions are embedded into the dynamics of the system. Shaping these nonlinear dynamics, according to recorded motions, allows for dynamical system to generalize beyond demonstration. As a result, the robot can generate motions of other situations not included in the recorded human demonstrations. In this dissertation, a Reservoir Computing approach is used to create a dynamical system in which such demonstrations are embedded. Reservoir Computing systems are Recurrent Neural Network-based approaches that are efficiently trained by considering only the training of the readout connections and retaining all other connections of such a network unchanged given their initial randomly chosen values. Although they have been used to embed periodic motions before, they were extended to embed discrete motions, or both. This work describes how such a motion pattern-generating system is built, investigates the nature of the underlying dynamics and evaluates their robustness in the face of perturbations. Additionally, a dynamical system approach to obstacle avoidance is proposed that is based on vector fields in the presence of repellers. This technique can be used to extend the motion abilities of the robot without need for changing the trained Motion Pattern Generator (MPG). Therefore, this approach can be applied in real-time on any system that generates a certain movement trajectory. Assume that the MPG system is implemented on an industrial robotic arm, similar to the ones used in a car factory. Even though the obstacle avoidance strategy presented is able to modify the generated motion of the robot’s gripper in such a way that it avoids obstacles, it does not guarantee that other parts of the robot cannot collide with a human. To prevent this, engineers have started to use advanced control algorithms that measure the amount of torque that is applied on the robot. This allows the robot to be aware of external perturbations. However, it turns out that, even with fast control loops, the adaptation to compensate for a sudden perturbation, is too slow to prevent high interaction forces. To reduce such forces, researchers started to use mechanical elements that are passively compliant (e.g., springs) and light-weight flexible materials to construct robots. Although such compliant robots are much safer and inherently energy efficient to use, their control becomes much harder. Most control approaches use model information about the robot (e.g., weight distribution and shape). However, when constructing a compliant robot it is hard to determine the dynamics of these materials. Therefore, a model-free adaptive control framework is proposed that assumes no prior knowledge about the robot. By interacting with the robot it learns an inverse robot model that is used as controller. The more it interacts, the better the control be- comes. Appropriately, this framework is called Inverse Modeling Adaptive (IMA) control framework. I have evaluated the IMA controller’s tracking ability on sev- eral tasks, investigating its model independence and stability. Furthermore, I have shown its fast learning ability and comparable performance to taskspecific designed controllers. Given both the MPG and IMA controllers, it is possible to improve the inter- actability of a compliant robot in a human-friendly environment. When the robot is to perform human-like motions for a large set of tasks, we need to demonstrate motion examples of all these tasks. However, biological research concerning the motion generation of animals and humans revealed that a limited set of motion patterns, called motion primitives, are modulated and combined to generate advanced motor/motion skills that humans and animals exhibit. Inspired by these interesting findings, I investigate if a single motion primitive indeed can be modulated to achieve a desired motion behavior. By some elementary experiments, where an MPG is controlled by an IMA controller, a proof of concept is presented. Furthermore, a general hierarchy is introduced that describes how a robot can be controlled in a biology-inspired manner. I also investigated how motion primitives can be combined to produce a desired motion. However, I was unable to get more advanced implementations to work. The results of some simple experiments are presented in the appendix. Another approach I investigated assumes that the primitives themselves are undefined. Instead, only a high-level description is given, which describes that every primitive on average should contribute equally, while still allowing for a single primitive to specialize in a part of the motion generation. Without defining the behavior of a primitive, only a set of untrained IMA controllers is used of which each will represent a single primitive. As a result of the high-level heuristic description, the task space is tiled into sub-regions in an unsupervised manner. Resulting in controllers that indeed represent a part of the motion generation. I have applied this Modular Architecture with Control Primitives (MACOP) on an inverse kinematic learning task and investigated the emerged primitives. Thanks to the tiling of the task space, it becomes possible to control redundant systems, because redundant solutions can be spread over several control primitives. Within each sub region of the task space, a specific control primitive is more accurate than in other regions allowing for the task complexity to be distributed over several less complex tasks. Finally, I extend the use of an IMA-controller, which is tracking controller, to the control of under-actuated systems. By using a sample-based planning algorithm it becomes possible to explore the system dynamics in which a path to a desired state can be planned. Afterwards, MACOP is used to incorporate feedback and to learn the necessary control commands corresponding to the planned state space trajectory, even if it contains errors. As a result, the under-actuated control of a cart pole system was achieved. Furthermore, I presented the concept of a simulation based control framework that allows the learning of the system dynamics, planning and feedback control iteratively and simultaneously

Effizientes und stabiles online Lernen für "Developmental Robots"

Recent progress in robotics and cognitive science has inspired a new generation of more versatile robots, so-called developmental robots. Many learning approaches for these robots are inspired by developmental processes and learning mechanisms observed in children. It is widely accepted that developmental robots must autonomously develop, acquire their skills, and cope with unforeseen challenges in unbounded environments through lifelong learning. Continuous online adaptation and intrinsically motivated learning are thus essential capabilities for these robots. However, the high sample-complexity of online learning and intrinsic motivation methods impedes the efficiency and practical feasibility of these methods for lifelong learning. Consequently, the majority of previous work has been demonstrated only in simulation. This thesis devises new methods and learning schemes to mitigate this problem and to permit direct online training on physical robots. A novel intrinsic motivation method is developed to drive the robot’s exploration to efficiently select what to learn. This method combines new knowledge-based and competence-based signals to increase sample-efficiency and to enable lifelong learning. While developmental robots typically acquire their skills through self-exploration, their autonomous development could be accelerated by additionally learning from humans. Yet there is hardly any research to integrate intrinsic motivation with learning from a teacher. The thesis therefore establishes a new learning scheme to integrate intrinsic motivation with learning from observation. The underlying exploration mechanism in the proposed learning schemes relies on Goal Babbling as a goal-directed method for learning direct inverse robot models online, from scratch, and in a learning while behaving fashion. Online learning of multiple solutions for redundant robots with this framework was missing. This thesis devises an incremental online associative network to enable simultaneous exploration and solution consolidation and establishes a new technique to stabilize the learning system. The proposed methods and learning schemes are demonstrated for acquiring reaching skills. Their efficiency, stability, and applicability are benchmarked in simulation and demonstrated on a physical 7-DoF Baxter robot arm.Jüngste Entwicklungen in der Robotik und den Kognitionswissenschaften haben zu einer Generation von vielseitigen Robotern geführt, die als ”Developmental Robots” bezeichnet werden. Lernverfahren für diese Roboter sind inspiriert von Lernmechanismen, die bei Kindern beobachtet wurden. ”Developmental Robots” müssen autonom Fertigkeiten erwerben und unvorhergesehene Herausforderungen in uneingeschränkten Umgebungen durch lebenslanges Lernen meistern. Kontinuierliches Anpassen und Lernen durch intrinsische Motivation sind daher wichtige Eigenschaften. Allerdings schränkt der hohe Aufwand beim Generieren von Datenpunkten die praktische Nutzbarkeit solcher Verfahren ein. Daher wurde ein Großteil nur in Simulationen demonstriert. In dieser Arbeit werden daher neue Methoden konzipiert, um dieses Problem zu meistern und ein direktes Online-Training auf realen Robotern zu ermöglichen. Dazu wird eine neue intrinsisch motivierte Methode entwickelt, die während der Umgebungsexploration effizient auswählt, was gelernt wird. Sie kombiniert neue wissens- und kompetenzbasierte Signale, um die Sampling-Effizienz zu steigern und lebenslanges Lernen zu ermöglichen. Während ”Developmental Robots” Fertigkeiten durch Selbstexploration erwerben, kann ihre Entwicklung durch Lernen durch Beobachten beschleunigt werden. Dennoch gibt es kaum Arbeiten, die intrinsische Motivation mit Lernen von interagierenden Lehrern verbinden. Die vorliegende Arbeit entwickelt ein neues Lernschema, das diese Verbindung schafft. Der in den vorgeschlagenen Lernmethoden genutzte Explorationsmechanismus beruht auf Goal Babbling, einer zielgerichteten Methode zum Lernen inverser Modelle, die online-fähig ist, kein Vorwissen benötigt und Lernen während der Ausführung von Bewegungen ermöglicht. Das Online-Lernen mehrerer Lösungen inverser Modelle redundanter Roboter mit Goal Babbling wurde bisher nicht erforscht. In dieser Arbeit wird dazu ein inkrementell lernendes, assoziatives neuronales Netz entwickelt und eine Methode konzipiert, die es stabilisiert. Das Netz ermöglicht deren gleichzeitige Exploration und Konsolidierung. Die vorgeschlagenen Verfahren werden für das Greifen nach Objekten demonstriert. Ihre Effizienz, Stabilität und Anwendbarkeit werden simulativ verglichen und mit einem Roboter mit sieben Gelenken demonstriert

Reliability of Extreme Learning Machines

Neumann K. Reliability of Extreme Learning Machines. Bielefeld: Bielefeld University Library; 2014.The reliable application of machine learning methods becomes increasingly important in challenging engineering domains. In particular, the application of extreme learning machines (ELM) seems promising because of their apparent simplicity and the capability of very efficient processing of large and high-dimensional data sets. However, the ELM paradigm is based on the concept of single hidden-layer neural networks with randomly initialized and fixed input weights and is thus inherently unreliable. This black-box character usually repels engineers from application in potentially safety critical tasks. The problem becomes even more severe since, in principle, only sparse and noisy data sets can be provided in such domains. The goal of this thesis is therefore to equip the ELM approach with the abilities to perform in a reliable manner. This goal is approached in three aspects by enhancing the robustness of ELMs to initializations, make ELMs able to handle slow changes in the environment (i.e. input drifts), and allow the incorporation of continuous constraints derived from prior knowledge. It is shown in several diverse scenarios that the novel ELM approach proposed in this thesis ensures a safe and reliable application while simultaneously sustaining the full modeling power of data-driven methods