Real Time Trajectory Prediction Using Deep Conditional Generative Models

Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it is still challenging to make long term accurate predictions with the low latency required by real time robotic systems. In this paper, we propose a deep conditional generative model for trajectory prediction that is learned from a data set of collected trajectories. Our method uses encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations. The encoder and decoder networks are trained using stochastic gradient variational Bayes. In the experiments, we show that our model provides more accurate long term predictions with a lower latency that popular models for trajectory forecasting like recurrent neural networks or physical models based on differential equations. Finally, we test our proposed approach in a robot table tennis scenario to evaluate the performance of the proposed method in a robotic task with hard real time constraints

Adaptive Robot Systems in Highly Dynamic Environments: A Table Tennis Robot

Hintergrund: Tischtennis bietet ideale Bedingungen, um Kamera-basierte Roboterarme am Limit zu testen. Die besondere Herausforderung liegt in der hohen Geschwindigkeit des Spiels und in der großen Varianz von Spin und Tempo jedes einzelnen Schlages. Die bisherige Forschung mit Tischtennisrobotern beschränkt sich jedoch auf einfache Szenarien, d.h. auf langsame Bälle mit einer geringen Rotation. Forschungsziel: Es soll ein lernfähiger Tischtennisroboter entwickelt werden, der mit dem Spin menschlicher Gegner umgehen kann. Methoden: Das vorgestellte Robotersystem besteht aus sechs Komponenten: Ballpositionserkennung, Ballspinerkennung, Balltrajektorienvorhersage, Schlagparameterbestimmung, Robotertrajektorienplanung und Robotersteuerung. Zuerst wird der Ball mit traditioneller Bildverarbeitung in den Kamerabildern lokalisiert. Mit iterativer Triangulation wird dann seine 3D-Position berechnet. Aus der Kurve der Ballpositionen wird die aktuelle Position und Geschwindigkeit des Balles ermittelt. Für die Spinerkennung werden drei Methoden präsentiert: Die ersten beiden verfolgen die Bewegung des aufgedruckten Ball-Logos auf hochauflösenden Bildern durch Computer Vision bzw. Convolutional Neural Networks. Im dritten Ansatz wird die Flugbahn des Balls unter Berücksichtigung der Magnus-Kraft analysiert. Anhand der Position, der Geschwindigkeit und des Spins des Balls wird die zukünftige Flugbahn berechnet. Dafür wird die physikalische Diffenzialgleichung mit Gravitationskraft, Luftwiderstandskraft und Magnus-Kraft schrittweise gelöst. Mit dem berechneten Zustand des Balls am Schlagpunkt haben wir einen Reinforcement-Learning-Algorithmus trainiert, der bestimmt, mit welchen Schlagparametern der Ball zu treffen ist. Eine passende Robotertrajektorie wird von der Reflexxes-Bibliothek generiert. %Der Roboter wird dann mit einer Frequenz von 250 Hz angesteuert. Ergebnisse: In der quantitativen Auswertung erzielen die einzelnen Komponenten mindestens so gute Ergebnisse wie vergleichbare Tischtennisroboter. Im Hinblick auf das Forschungsziel konnte der Roboter - ein Konterspiel mit einem Menschen führen, mit bis zu 60 Rückschlägen, - unterschiedlichen Spin (Über- und Unterschnitt) retournieren - und mehrere Tischtennisübungen innerhalb von 200 Schlägen erlernen. Schlußfolgerung: Bedeutende algorithmische Neuerungen führen wir in der Spinerkennung und beim Reinforcement Learning von Schlagparametern ein. Dadurch meistert der Roboter anspruchsvollere Spin- und Übungsszenarien als in vergleichbaren Arbeiten.Background: Robotic table tennis systems offer an ideal platform for pushing camera-based robotic manipulation systems to the limit. The unique challenge arises from the fast-paced play and the wide variation in spin and speed between strokes. The range of scenarios under which existing table tennis robots are able to operate is, however, limited, requiring slow play with low rotational velocity of the ball (spin). Research Goal: We aim to develop a table tennis robot system with learning capabilities able to handle spin against a human opponent. Methods: The robot system presented in this thesis consists of six components: ball position detection, ball spin detection, ball trajectory prediction, stroke parameter suggestion, robot trajectory generation, and robot control. For ball detection, the camera images pass through a conventional image processing pipeline. The ball’s 3D positions are determined using iterative triangulation and these are then used to estimate the current ball state (position and velocity). We propose three methods for estimating the spin. The first two methods estimate spin by analyzing the movement of the logo printed on the ball on high-resolution images using either conventional computer vision or convolutional neural networks. The final approach involves analyzing the trajectory of the ball using Magnus force fitting. Once the ball’s position, velocity, and spin are known, the future trajectory is predicted by forward-solving a physical ball model involving gravitational, drag, and Magnus forces. With the predicted ball state at hitting time as state input, we train a reinforcement learning algorithm to suggest the racket state at hitting time (stroke parameter). We use the Reflexxes library to generate a robot trajectory to achieve the suggested racket state. Results: Quantitative evaluation showed that all system components achieve results as good as or better than comparable robots. Regarding the research goal of this thesis, the robot was able to - maintain stable counter-hitting rallies of up to 60 balls with a human player, - return balls with different spin types (topspin and backspin) in the same rally, - learn multiple table tennis drills in just 200 strokes or fewer. Conclusion: Our spin detection system and reinforcement learning-based stroke parameter suggestion introduce significant algorithmic novelties. In contrast to previous work, our robot succeeds in more difficult spin scenarios and drills

Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.

Autonomous and Intrinsically Motivated Robots for Sustained Human-Robot Interaction

A challenge in using fully autonomous robots in human-robot interaction (HRI) is to design behavior that is engaging enough to encourage voluntary, long-term interaction, yet robust to the perturbations induced by human interaction. It has been repeatedly argued that intrinsic motivations (IMs) are crucial for human development, so it seems reasonable that this mechanism could produce an adaptive and developing robot, which is interesting to interact with. This thesis evaluates whether an intrinsically motivated robot can lead to sustained HRI. Recent research showed that robots which ‘appeared’ intrinsically motivated raised interest in the human interaction partner. The displayed IMs resulted from ‘unpredictably’ asking a question or from a self-disclosing statement. They were designed with the help of pre-defined scripts or teleoperation. An issue here is that this practice renders the behavior less robust toward unexpected input or requires a trained human in the loop. Instead, this thesis proposes a computational model of IM to realize fully autonomous and adaptive behavior generation in a robot. Previous work showed that predictive information maximization leads to playful, exploratory behavior in simulated robots that is robust to changes in the robot’s morphology and environment. This thesis demonstrates how to deploy the formalism on a physical robot that interacts with humans. The thesis conducted three within-subjects studies, where participants interacted with a fully autonomous Sphero BB8 robot with two behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive. The first study contributes to the idea of the overall proposed study design: the interaction needs to be designed in such a way, that participants are not given any idea of the robot’s task. The second study implements this idea, letting participants focus on answering the question of whether the robots are any different. It further contributes ideas for a more ‘challenging’ baseline behavior motivating the third and final study. Here, a systematic baseline is generated and shows that participants perceive it as almost indistinguishable and similarly animated compared to the intrinsically motivated robot. Despite the emphasis on the design of similarly perceived baseline behaviors, quantitative analyses of post-interaction questionnaires after each study showed a significantly higher perception of the dimension ‘Warmth’ for the intrinsically motivated robot compared to the baseline behavior. Warmth is considered a primary dimension for social attitude formation in social cognition. A human perceived as warm (i.e. friendly and trustworthy) experiences more positive social interactions. The Robotic Social Attribute Scale (RoSAS) implements the scale dimension Warmth for the HRI domain, which has been validated with a series of still images. Going beyond static images, this thesis provides support for the use and applicability of this scale dimension for the purpose of comparing behaviors. It shows that participants prefer to continue interacting with the robot they perceive highest in Warmth. This research opens new research avenues, in particular with respect to different physical robots and longitudinal studies, which are ought to be performed to corroborate the results presented here. However, this thesis shows the general methods presented here, which do not require a human operator in the loop, can be used to imbue robots with behavior leading to positive perception by their human interaction partners, which can yield sustained HRI

Design and training of deep reinforcement learning agents

Deep reinforcement learning is a field of research at the intersection of reinforcement learning and deep learning. On one side, the problem that researchers address is the one of reinforcement learning: to act efficiently. A large number of algorithms were developed decades ago in this field to update value functions and policies, explore, and plan. On the other side, deep learning methods provide powerful function approximators to address the problem of representing functions such as policies, value functions, and models. The combination of ideas from these two fields offers exciting new perspectives. However, building successful deep reinforcement learning experiments is particularly difficult due to the large number of elements that must be combined and adjusted appropriately. This thesis proposes a broad overview of the organization of these elements around three main axes: agent design, environment design, and infrastructure design. Arguably, the success of deep reinforcement learning research is due to the tremendous amount of effort that went into each of them, both from a scientific and engineering perspective, and their diffusion via open source repositories. For each of these three axes, a dedicated part of the thesis describes a number of related works that were carried out during the doctoral research. The first part, devoted to the design of agents, presents two works. The first one addresses the problem of applying discrete action methods to large multidimensional action spaces. A general method called action branching is proposed, and its effectiveness is demonstrated with a novel agent, named BDQ, applied to discretized continuous action spaces. The second work deals with the problem of maximizing the utility of a single transition when learning to achieve a large number of goals. In particular, it focuses on learning to reach spatial locations in games and proposes a new method called Q-map to do so efficiently. An exploration mechanism based on this method is then used to demonstrate the effectiveness of goal-directed exploration. Elements of these works cover some of the main building blocks of agents: update methods, neural architectures, exploration strategies, replays, and hierarchy. The second part, devoted to the design of environments, also presents two works. The first one shows how various tasks and demonstrations can be combined to learn complex skill spaces that can then be reused to solve even more challenging tasks. The proposed method, called CoMic, extends previous work on motor primitives by using a single multi-clip motion capture tracking task in conjunction with complementary tasks targeting out-of-distribution movements. The second work addresses a particular type of control method vastly neglected in traditional environments but essential for animals: muscle control. An open source codebase called OstrichRL is proposed, containing a musculoskeletal model of an ostrich, an ensemble of tasks, and motion capture data. The results obtained by training a state-of-the-art agent on the proposed tasks show that controlling such a complex system is very difficult and illustrate the importance of using motion capture data. Elements of these works demonstrate the meticulous work that must go into designing environment parts such as: models, observations, rewards, terminations, resets, steps, and demonstrations. The third part, on the design of infrastructures, presents three works. The first one explains the difference between the types of time limits commonly used in reinforcement learning and why they are often treated inappropriately. In one case, tasks are time-limited by nature and a notion of time should be available to agents to maintain the Markov property of the underlying decision process. In the other case, tasks are not time-limited by nature, but time limits are used for convenience to diversify experiences. This is the most common case. It requires a distinction between time limits and environmental terminations, and bootstrapping should be performed at the end of partial episodes. The second work proposes to unify the most popular deep learning frameworks using a single library called Ivy, and provides new differentiable and framework-agnostic libraries built with it. Four such code bases are provided for gradient-based robot motion planning, mechanics, 3D vision, and differentiable continuous control environments. Finally, the third paper proposes a novel deep reinforcement learning library, called Tonic, built with simplicity and modularity in mind, to accelerate prototyping and evaluation. In particular, it contains implementations of several continuous control agents and a large-scale benchmark. Elements of these works illustrate the different components to consider when building the infrastructure for an experiment: deep learning framework, schedules, and distributed training. Added to these are the various ways to perform evaluations and analyze results for meaningful, interpretable, and reproducible deep reinforcement learning research.Open Acces

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering