Modeling and Learning of Complex Motor Tasks: A Case Study with Robot Table Tennis

Most tasks that humans need to accomplished in their everyday life require certain motor skills. Although most motor skills seem to rely on the same elementary movements, humans are able to accomplish many different tasks. Robots, on the other hand, are still limited to a small number of skills and depend on well-defined environments. Modeling new motor behaviors is therefore an important research area in robotics. Computational models of human motor control are an essential step to construct robotic systems that are able to solve complex tasks in a human inhabited environment. These models can be the key for robust, efficient, and human-like movement plans. In turn, the reproduction of human-like behavior on a robotic system can be also beneficial for computational neuroscientists to verify their hypotheses. Although biomimetic models can be of great help in order to close the gap between human and robot motor abilities, these models are usually limited to the scenarios considered. However, one important property of human motor behavior is the ability to adapt skills to new situations and to learn new motor skills with relatively few trials. Domain-appropriate machine learning techniques, such as supervised and reinforcement learning, have a great potential to enable robotic systems to autonomously learn motor skills. In this thesis, we attempt to model and subsequently learn a complex motor task. As a test case for a complex motor task, we chose robot table tennis throughout this thesis. Table tennis requires a series of time critical movements which have to be selected and adapted according to environmental stimuli as well as the desired targets. We first analyze how humans play table tennis and create a computational model that results in human-like hitting motions on a robot arm. Our focus lies on generating motor behavior capable of adapting to variations and uncertainties in the environmental conditions. We evaluate the resulting biomimetic model both in a physically realistic simulation and on a real anthropomorphic seven degrees of freedom Barrett WAM robot arm. This biomimetic model based purely on analytical methods produces successful hitting motions, but does not feature the flexibility found in human motor behavior. We therefore suggest a new framework that allows a robot to learn cooperative table tennis from and with a human. Here, the robot first learns a set of elementary hitting movements from a human teacher by kinesthetic teach-in, which is compiled into a set of motor primitives. To generalize these movements to a wider range of situations we introduce the mixture of motor primitives algorithm. The resulting motor policy enables the robot to select appropriate motor primitives as well as to generalize between them. Furthermore, it also allows to adapt the selection process of the hitting movements based on the outcome of previous trials. The framework is evaluated both in simulation and on a real Barrett WAM robot. In consecutive experiments, we show that our approach allows the robot to return balls from a ball launcher and furthermore to play table tennis with a human partner. Executing robot movements using a biomimetic or learned approach enables the robot to return balls successfully. However, in motor tasks with a competitive goal such as table tennis, the robot not only needs to return the balls successfully in order to accomplish the task, it also needs an adaptive strategy. Such a higher-level strategy cannot be programed manually as it depends on the opponent and the abilities of the robot. We therefore make a first step towards the goal of acquiring such a strategy and investigate the possibility of inferring strategic information from observing humans playing table tennis. We model table tennis as a Markov decision problem, where the reward function captures the goal of the task as well as knowledge on effective elements of a basic strategy. We show how this reward function, and therefore the strategic information can be discovered with model-free inverse reinforcement learning from human table tennis matches. The approach is evaluated on data collected from players with different playing styles and skill levels. We show that the resulting reward functions are able to capture expert-specific strategic information that allow to distinguish the expert among players with different playing skills as well as different playing styles. To summarize, in this thesis, we have derived a computational model for table tennis that was successfully implemented on a Barrett WAM robot arm and that has proven to produce human-like hitting motions. We also introduced a framework for learning a complex motor task based on a library of demonstrated hitting primitives. To select and generalize these hitting movements we developed the mixture of motor primitives algorithm where the selection process can be adapted online based on the success of the synthesized hitting movements. The setup was tested on a real robot, which showed that the resulting robot table tennis player is able to play a cooperative game against an human opponent. Finally, we could show that it is possible to infer basic strategic information in table tennis from observing matches of human players using model-free inverse reinforcement learning

Jointly learning trajectory generation and hitting point prediction in robot table tennis

This paper proposes a combined learning framework for a table tennis robot. In a typical robot table tennis setup, a single striking point is predicted for the robot on the basis of the ball's initial state. Subsequently, the desired Cartesian racket state and the desired joint states at the striking time are determined. Finally, robot joint trajectories are generated. Instead of predicting a single striking point, we propose to construct a ball trajectory prediction map, which predicts the ball's entire rebound trajectory using the ball's initial state. We construct as well a robot trajectory generation map, which predicts the robot joint movement pattern and the movement duration using the Cartesian racket trajectories without the need of inverse kinematics, where a correlation function is used to adapt these joint movement parameters according to the ball flight trajectory. With joint movement parameters, we can directly generate joint trajectories. Additionally, we introduce a reinforcement learning approach to modify robot joint trajectories such that the robot can return balls well. We validate this new framework in both the simulated and the real robotic systems and illustrate that a seven degree-of-freedom Barrett WAM robot performs well

Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Anticipation is crucial for fluent human-robot interaction, which allows a robot to independently coordinate its actions with human beings in joint activities. An anticipatory robot relies on a predictive model of its human partners, and selects its own action according to the model's predictions. Intention inference and decision making are key elements towards such anticipatory robots. In this thesis, we present a machine-learning approach to intention inference and decision making, based on Hierarchical Gaussian Process Dynamics Models (H-GPDMs). We first introduce the H-GPDM, a class of generic latent-variable dynamics models. The H-GPDM represents the generative process of complex human movements that are directed by exogenous driving factors. Incorporating the exogenous variables in the dynamics model, the H-GPDM achieves improved interpretation, analysis, and prediction of human movements. While exact inference of the exogenous variables and the latent states is intractable, we introduce an approximate method using variational Bayesian inference, and demonstrate the merits of the H-GPDM in three different applications of human movement analysis. The H-GPDM lays a foundation for the following studies on intention inference and decision making. Intention inference is an essential step towards anticipatory robots. For this purpose, we consider a special case of the H-GPDM, the Intention-Driven Dynamics Model (IDDM), which considers the human partners' intention as exogenous driving factors. The IDDM is applicable to intention inference from observed movements using Bayes' theorem, where the latent state variables are marginalized out. As most robotics applications are subject to real-time constraints, we introduce an efficient online algorithm that allows for real-time intention inference. We show that the IDDM achieved state-of-the-art performance in intention inference using two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive robots. Decision making based on a time series of predictions allows a robot to be proactive in its action selection, which involves a trade-off between the accuracy and confidence of the prediction and the time for executing a selected action. To address the problem of action selection and optimal timing for initiating the movement, we formulate the anticipatory action selection using Partially Observable Markov Decision Process, where the H-GPDM is adopted to update belief state and to estimate transition model. We present two approaches to policy learning and decision making, and show their effectiveness using human-robot table tennis. In addition, we consider decision making solely based on the preference of the human partners, where observations are not sufficient for reliable intention inference. We formulate it as a repeated game and present a learning approach to safe strategies that exploit the humans' preferences. The learned strategy enables action selection when reliable intention inference is not available due to insufficient observation, e.g., for a robot to return served balls from a human table tennis player. In this thesis, we use human-robot table tennis as a running example, where a key bottleneck is the limited amount of time for executing a hitting movement. Movement initiation usually requires an early decision on the type of action, such as a forehand or backhand hitting movement, at least 80ms before the opponent has hit the ball. The robot, therefore, needs to be anticipatory and proactive of the opponent's intended target. Using the proposed methods, the robot can predict the intended target of the opponent and initiate an appropriate hitting movement according to the prediction. Experimental results show that the proposed intention inference and decision making methods can substantially enhance the capability of the robot table tennis player, using both a physically realistic simulation and a real Barrett WAM robot arm with seven degrees of freedom

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

Tennis expertise reduces costs in cognition but not in motor skills in a cognitive-motor dual-task condition

Dual-process theories predict performance reductions under dual-task situations (= situations where two tasks have to be processed and executed simultaneously), because limited cognitive resources have to be shared between concurrent tasks. Increases in expertise should reduce the attentional resources needed to perform a motor task, leading to reduced dual-task costs. The current studies investigated whether expert tennis players (performance ratings of 1 to 14 in the German system) show smaller costs compared to intermediate players (performance ratings of 15 to 23). Two studies assessed single- and dual-task performance in a within-subject design in the same tennis task, returning balls into a target field. Two different cognitive tasks were used, a 3-back working memory task in study 1, and a vocabulary-learning task (episodic memory) in study 2. As predicted, performance in both cognitive tasks was reduced during dual-tasking, while the accuracy of tennis returns remained stable under cognitive challenge. These findings indicate that skilled tennis players show a task-prioritization strategy in favor of the tennis task in a dual-task situation. In study 1, intermediate players showed higher overall dual-task costs than experts, but the group differences in dual-task costs did not reach significance in study 2. This may have been due to less pronounced expertise-differences between the groups in study 2. The findings replicate and extend previous expertise studies in sports to the domain of tennis. We argue that an athlete's ability to keep up cognitive and motor performances in challenging dual-task situations may be a valid indicator of skill level

Dynamic Bat-Control of a Redundant Ball Playing Robot

This thesis shows a control algorithm for coping with a ball batting task for an entertainment robot. The robot is a three jointed robot with a redundant degree of freedom and its name is Doggy . Doggy because of its dog-like costume. Design, mechanics and electronics were developed by us. DC-motors control the tooth belt driven joints, resulting in elasticities between the motor and link. Redundancy and elasticity have to be taken into account by our developed controller and are demanding control tasks. In this thesis we show the structure of the ball playing robot and how this structure can be described as a model. We distinguish two models: One model that includes a flexible bearing, the other does not. Both models are calibrated using the toolkit Sparse Least Squares on Manifolds (SLOM) - i.e. the parameters for the model are determined. Both calibrated models are compared to measurements of the real system. The model with the flexible bearing is used to implement a state estimator - based on a Kalman filter - on a microcontroller. This ensures real time estimation of the robot states. The estimated states are also compared with the measurements and are assessed. The estimated states represent the measurements well. In the core of this work we develop a Task Level Optimal Controller (TLOC), a model-predictive optimal controller based on the principles of a Linear Quadratic Regulator (LQR). We aim to play a ball back to an opponent precisely. We show how this task of playing a ball at a desired time with a desired velocity at a desired position can be embedded into the LQR principle. We use cost functions for the task description. In simulations, we show the functionality of the control concept, which consists of a linear part (on a microcontroller) and a nonlinear part (PC software). The linear part uses feedback gains which are calculated by the nonlinear part. The concept of the ball batting controller with precalculated feedback gains is evaluated on the robot. This shows successful batting motions. The entertainment aspect has been tested on the Open Campus Day at the University of Bremen and is summarized here shortly. Likewise, a jointly developed audience interaction by recognition of distinctive sounds is summarized herein. In this thesis we answer the question, if it is possible to define a rebound task for our robot within a controller and show the necessary steps for this

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Physiology, biomechanics and injuries in table tennis: A systematic review

Objectives Table tennis is a widely practiced sport, often described as a reaction sport. Therefore, players need to practice extensively that may expose them to overuse injuries. For optimizing training with limitation of the injury risk, the knowledges of table tennis physiology, biomechanics and epidemiology are of primary interest. Methods For that purpose, a literature review has been made through a systematic search on three scientific databases. Overall, table tennis physiology is complex due to intense and intermittent efforts. It results that some technological challenges still need to be addressed to accurately quantify this physiology. Besides, current rules changes may modify the table tennis physiological requirements. Results Findings in neurophysiology tend to define table tennis as an anticipation sport rather than a reaction sport and higher occulo-motor skills were found in table tennis population with respect to average population. Regarding biomechanics, some rare studies have been done but none had investigated the energy flow between the upper- and the lower-body, which would be interesting to understand how the energy generated by the footwork contributes to racket velocity. Conclusion Finally, epidemiological studies lack of details on injury locations and diagnosis. These data could be of high interest to improve medical and training care