A survey on policy search algorithms for learning robot controllers in a handful of trials

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on Robotic

A survey on policy search algorithms for learning robot controllers in a handful of trials

International audienceMost policy search (PS) algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word “big-data,” we refer to this challenge as “micro-data reinforcement learning.” In this article, we show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based PS), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots, designing generic priors, and optimizing the computing time

Actuation-Aware Simplified Dynamic Models for Robotic Legged Locomotion

In recent years, we witnessed an ever increasing number of successful hardware implementations of motion planners for legged robots. If one common property is to be identified among these real-world applications, that is the ability of online planning. Online planning is forgiving, in the sense that it allows to relentlessly compensate for external disturbances of whatever form they might be, ranging from unmodeled dynamics to external pushes or unexpected obstacles and, at the same time, follow user commands. Initially replanning was restricted only to heuristic-based planners that exploit the low computational effort of simplified dynamic models. Such models deliberately only capture the main dynamics of the system, thus leaving to the controllers the issue of anchoring the desired trajectory to the whole body model of the robot. In recent years, however, we have seen a number of new approaches attempting to increase the accuracy of the dynamic formulation without trading-off the computational efficiency of simplified models. In this dissertation, as an example of successful hardware implementation of heuristics and simplified model-based locomotion, I describe the framework that I developed for the generation of an omni-directional bounding gait for the HyQ quadruped robot. By analyzing the stable limit cycles for the sagittal dynamics and the Center of Pressure (CoP) for the lateral stabilization, the described locomotion framework is able to achieve a stable bounding while adapting to terrains of mild roughness and to sudden changes of the user desired linear and angular velocities. The next topic reported and second contribution of this dissertation is my effort to formulate more descriptive simplified dynamic models, without trading off their computational efficiency, in order to extend the navigation capabilities of legged robots to complex geometry environments. With this in mind, I investigated the possibility of incorporating feasibility constraints in these template models and, in particular, I focused on the joint torques limits which are usually neglected at the planning stage. In this direction, the third contribution discussed in this thesis is the formulation of the so called actuation wrench polytope (AWP), defined as the set of feasible wrenches that an articulated robot can perform given its actuation limits. Interesected with the contact wrench cone (CWC), this yields a new 6D polytope that we name feasible wrench polytope (FWP), defined as the set of all wrenches that a legged robot can realize given its actuation capabilities and the friction constraints. Results are reported where, thanks to efficient computational geometry algorithms and to appropriate approximations, the FWP is employed for a one-step receding horizon optimization of center of mass trajectory and phase durations given a predefined step sequence on rough terrains. For the sake of reachable workspace augmentation, I then decided to trade off the generality of the FWP formulation for a suboptimal scenario in which a quasi-static motion is assumed. This led to the definition of the, so called, local/instantaneous actuation region and of the global actuation/feasible region. They both can be seen as different variants of 2D linear subspaces orthogonal to gravity where the robot is guaranteed to place its own center of mass while being able to carry its own body weight given its actuation capabilities. These areas can be intersected with the well known frictional support region, resulting in a 2D linear feasible region, thus providing an intuitive tool that enables the concurrent online optimization of actuation consistent CoM trajectories and target foothold locations on rough terrains

Design of high-performance legged robots: A case study on a hopping and balancing robot

The availability and capabilities of present-day technology suggest that legged robots should be able to physically outperform their biological counterparts. This thesis revolves around the philosophy that the observed opposite is caused by over-complexity in legged robot design, which is believed to substantially suppress design for high-performance. In this dissertation a design philosophy is elaborated with a focus on simple but high performance design. This philosophy is governed by various key points, including holistic design, technology-inspired design, machine and behaviour co-design and design at the performance envelope. This design philosophy also focuses on improving progress in robot design, which is inevitably complicated by the aspire for high performance. It includes an approach of iterative design by trial-and-error, which is believed to accelerate robot design through experience. This thesis mainly focuses on the case study of Skippy, a fully autonomous monopedal balancing and hopping robot. Skippy is maximally simple in having only two actuators, which is the minimum number of actuators required to control a robot in 3D. Despite its simplicity, it is challenged with a versatile set of high-performance activities, ranging from balancing to reaching record jump heights, to surviving crashes from several meters and getting up unaided after a crash, while being built from off-the-shelf technology. This thesis has contributed to the detailed mechanical design of Skippy and its optimisations that abide the design philosophy, and has resulted in a robust and realistic design that is able to reach a record jump height of 3.8m. Skippy is also an example of iterative design through trial-and-error, which has lead to the successful design and creation of the balancing-only precursor Tippy. High-performance balancing has been successfully demonstrated on Tippy, using a recently developed balancing algorithm that combines the objective of tracking a desired position command with balancing, as required for preparing hopping motions. This thesis has furthermore contributed to several ideas and theories on Skippy's road of completion, which are also useful for designing other high-performance robots. These contributions include (1) the introduction of an actuator design criterion to maximize the physical balance recovery of a simple balancing machine, (2) a generalization of the centre of percussion for placement of components that are sensitive to shock and (3) algebraic modelling of a non-linear high-gravimetric energy density compression spring with a regressive stress-strain profile. The activities performed and the results achieved have been proven to be valuable, however they have also delayed the actual creation of Skippy itself. A possible explanation for this happening is that Skippy's requirements and objectives were too ambitious, for which many complications were encountered in the decision-making progress of the iterative design strategy, involving trade-offs between exercising trial-and-error, elaborate simulation studies and the development of above-mentioned new theories. Nevertheless, from (1) the resulting realistic design of Skippy, (2) the successful creation and demonstrations of Tippy and (3) the contributed theories for high-performance robot design, it can be concluded that the adopted design philosophy has been generally successful. Through the case study design project of the hopping and balancing robot Skippy, it is shown that proper design for high physical performance (1) can indeed lead to a robot design that is capable of physically outperforming humans and animals and (2) is already very challenging for a robot that is intended to be very simple

Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments

Ground robots capable of navigating a wide range of terrains are needed in several domains such as disaster response or planetary exploration. Hybrid driving-stepping locomotion is promising since it combines the complementary strengths of the two locomotion modes. However, suitable platforms require complex kinematic capabilities which need to be considered in corresponding locomotion planning methods. High terrain complexities induce further challenges for the planning problem. We present a search-based hybrid driving-stepping locomotion planning approach for robots which possess a quadrupedal base with legs ending in steerable wheels allowing for omnidirectional driving and stepping. Driving is preferred on sufficiently flat terrain while stepping is considered in the vicinity of obstacles. Steps are handled in a hierarchical manner: while only the connection between suitable footholds is considered during planning, those steps in the resulting path are expanded to detailed motion sequences considering the robot stability. To enable precise locomotion in challenging terrain, the planner takes the individual robot footprint into account. The method is evaluated in simulation and in real-world applications with the robots Momaro and Centauro. The results indicate that the planner provides bounded sub-optimal paths in feasible time. However, the required fine resolution and high-dimensional robot representation result in too large state spaces for more complex scenarios exceeding computation time and memory constraints. To enable the planner to be applicable in those scenarios, the method is extended to incorporate three levels of representation. In the vicinity of the robot, the detailed representation is used to obtain reliable paths for the near future. With increasing distance from the robot, the resolution gets coarser and the degrees of freedom of the robot representation decrease. To compensate this loss of information, those representations are enriched with additional semantics increasing the scene understanding. We further present how the most abstract representation can be used to generate an informed heuristic. Evaluation shows that planning is accelerated by multiple orders of magnitude with comparable result quality. However, manually designing the additional representations and tuning the corresponding cost functions requires a high effort. Therefore, we present a method to support the generation of an abstract representation through a convolutional neural network (CNN). While a low-dimensional, coarse robot representation and corresponding action set can be easily defined, a CNN is trained on artificially generated data to represent the abstract cost function. Subsequently, the abstract representation can be used to generate a similar informed heuristic, as described above. The CNN evaluation on multiple data sets indicates that the learned cost function generalizes well to realworld scenes and that the abstraction quality outperforms the manually tuned approach. Applied to hybrid driving-stepping locomotion planning, the heuristic achieves similar performance while design and tuning efforts are minimized. Since a learning-based method turned out to be beneficial to support the search-based planner, we finally investigate if the whole planning problem can be solved by a learning-based approach. Value Iteration Networks (VINs) are known to show good generalizability and goal-directed behavior, while being limited to small state spaces. Inspired by the above-described results, we extend VINs to incorporate multiple levels of abstraction to represent larger planning problems with suitable state space sizes. Experiments in 2D grid worlds show that this extension enables VINs to solve significantly larger planning tasks. We further apply the method to omnidirectional driving of the Centauro robot in cluttered environments which indicates limitations but also emphasizes the future potential of learning-based planning methods.Planung von Hybrider Fahr-Lauf-Lokomotion für Bodenroboter in Anspruchsvollen Umgebungen Bodenroboter, welche eine Vielzahl von Untergründen überwinden können, werden in vielen Anwendungsgebieten benötigt. Beispielszenarien sind die Katastrophenhilfe oder Erkundungsmissionen auf fremden Planeten. In diesem Kontext ist hybride Fahr-/Lauf-Fortbewegung vielversprechend, da sie die sich ergänzenden Stärken der beiden Fortbewegungsarten miteinander vereint. Um dies zu realisieren benötigen entsprechende Roboter allerdings komplexe kinematische Fähigkeiten, welche auch in adäquaten Ansätzen für die Planung dieser Fortbewegung berücksichtigt werden müssen. Anspruchsvolle Umgebungen mit komplexen Untergründen erhöhen dabei zusätzlich die Anforderungen an die Bewegungsplanung. In dieser Arbeit wird ein suchbasierter Ansatz für kombinierte Fahr-/Lauf-Fortbewegungsplanung vorgestellt. Die adressierten Zielplattformen sind vierbeinige Roboter, deren Beine in lenkbaren Rädern enden, so dass sie omnidirektional fahren und laufen können. Auf ausreichend ebenem Untergrund wird generell Fahren bevorzugt, während der Planer Laufmanöver in der Nähe von Hindernissen in Erwägung zieht. Schritte werden dabei in einer hierarchischen Art undWeise realisiert: Während des Planens werden nur Verbindungen zwischen geeigneten Auftrittsflächen gesucht. Nur solche Schritte, die im Ergebnispfad enthalten sind, werden anschließend zu detaillierten Bewegungsabläufen verfeinert, welche die Balance des Roboters sicherstellen. Um präzise Fortbewegung in anspruchsvollen Umgebungen zu ermöglichen, betrachtet der Planer die spezifischen Aufstandsflächen der vier Füße. Der Ansatz wurde sowohl in simulierten als auch in realen Tests mit den Robotern Momaro und Centauro evaluiert, wobei der Planer in der Lage war, Lösungspfade von ausreichender Qualität in zulässiger Zeit zu generieren. Allerdings ergeben die benötigte feine Planungsauflösung und die hochdimensionale Roboterrepräsentation große Zustandsräumen. Diese würden für komplexere oder größere Planungsprobleme die zulässige Rechenzeit und den verfügbaren Speicher überschreiten. Damit der Planer auch eben diese komplexeren oder größeren Planungsprobleme handhaben kann, wird eine Erweiterung des Ansatzes beschrieben, welche mehrere Repräsentationslevel mit einbezieht. In unmittelbarer Umgebung des Roboters wird die zuvor beschriebene detaillierte Repräsentation genutzt, um hochwertige Pfade für die nahe Zukunft zu erzeugen. Mit zunehmendem Abstand vom Roboter wird die Auflösung gröber und die Anzahl der Freiheitsgrade in der Roboterrepräsentation sinkt. Um den mit dieser Vergröberung einhergehenden Informationsverlust zu kompensieren, werden diese Repräsentationen mit zusätzlicher Semantik ausgestattet, welche das Szenenverständnis erhöht. Darüber hinaus wird beschrieben, wie die Repräsentation mit dem höchsten Abstraktionsgrad zur Berechnung einer effektiven Heuristik genutzt werden kann. Die Evaluation in Simulationsumgebungen zeigt, dass der Planungsprozess um mehrere Größenordnungen beschleunigt werden kann, während die Ergebnisqualität vergleichbar bleibt. Allerdings sind das manuelle Gestalten der zusätzlichen Repräsentationen und das dazugehörige Parametrisieren der Kostenfunktionen sehr arbeitsintensiv. Um diesen Aufwand zu reduzieren, wird daher eine Methode beschrieben, welche die Gestaltung einer abstrakten Repräsentation durch ein Convolutional Neural Network (CNN) unterstützt. Während eine grobe, niedrigdimensionale Roboterrepräsentation und ein dazugehöriges Aktionsset einfach definiert werden können, wird ein CNN auf künstlich erzeugten Daten trainiert, um die abstrakte Kostenfunktion zu lernen. Anschließend kann die so erzeugte abstrakte Repräsentation genutzt werden, um die bereits zuvor erwähnte effektive Heuristik zu berechnen. In der Evaluation des CNNs auf verschiedenen Datensätzen zeigt sich, dass die gelernte Kostenfunktion auch mit Daten aus realen Umgebungen funktioniert und dass die generelle Ergebnisqualität oberhalb der Ergebnisse mit manuell erzeugten Repräsentationen liegt. Die Anwendnung der Methode zur Planung hybrider Fahr-/Lauf-Fortbewegung zeigt, dass die so erzeugte Heuristik gleichwertige Ergebnisse wie die Heuristik auf Basis manuell erzeugter Repräsentation liefert, während der Aufwand zur Gestaltung und Parametrisierung deutlich verringert wurde. Da sich gezeigt hat, dass eine lernbasierte Methode den klassischen suchbasierten Ansatz effektiv unterstützen kann, wird in dieser Arbeit abschließend untersucht, ob das gesamte Planungsproblem durch eine lernbasierte Methode gelöst werden kann. Value Iteration Networks (VINs) sind in diesem Zusammenhang ein vielversprechender Ansatz, da sie bekanntlich ein gutes zielorientiertes Planungsverhalten lernen und das Gelernte auf unbekannte Situationen verallgemeinern können. Allerdings ist ihre bisherige Anwendung auf kleine Zustandsräume begrenzt. Durch die zuvor beschriebenen Ergebnisse motiviert, wird eine Erweiterung von VINs beschrieben, so dass diese auf verschiedenen Abstraktionsleveln planen, um größere Planungsprobleme in Zustandsräumen entsprechender Größe darzustellen. Experimente in 2D-Rasterumgebungen zeigen, dass die beschriebene Methode VINs in die Lage versetzt, deutlich größere Planungsprobleme zu lösen. Darüber hinaus wird die beschriebene Methode benutzt, um omnidirektionale Fahrmanöver für den Centauro-Roboter in anspruchsvollen Umgebungen zu planen. Gleichzeitig werden hier aber auch die momentanen, hardware-bedingten Grenzen rein lernbasierter Ansätze sowie ihr zukünftiges Potential aufgezeigt

Perception-driven approaches to real-time remote immersive visualization

In remote immersive visualization systems, real-time 3D perception through RGB-D cameras, combined with modern Virtual Reality (VR) interfaces, enhances the user’s sense of presence in a remote scene through 3D reconstruction rendered in a remote immersive visualization system. Particularly, in situations when there is a need to visualize, explore and perform tasks in inaccessible environments, too hazardous or distant. However, a remote visualization system requires the entire pipeline from 3D data acquisition to VR rendering satisfies the speed, throughput, and high visual realism. Mainly when using point-cloud, there is a fundamental quality difference between the acquired data of the physical world and the displayed data because of network latency and throughput limitations that negatively impact the sense of presence and provoke cybersickness. This thesis presents state-of-the-art research to address these problems by taking the human visual system as inspiration, from sensor data acquisition to VR rendering. The human visual system does not have a uniform vision across the field of view; It has the sharpest visual acuity at the center of the field of view. The acuity falls off towards the periphery. The peripheral vision provides lower resolution to guide the eye movements so that the central vision visits all the interesting crucial parts. As a first contribution, the thesis developed remote visualization strategies that utilize the acuity fall-off to facilitate the processing, transmission, buffering, and rendering in VR of 3D reconstructed scenes while simultaneously reducing throughput requirements and latency. As a second contribution, the thesis looked into attentional mechanisms to select and draw user engagement to specific information from the dynamic spatio-temporal environment. It proposed a strategy to analyze the remote scene concerning the 3D structure of the scene, its layout, and the spatial, functional, and semantic relationships between objects in the scene. The strategy primarily focuses on analyzing the scene with models the human visual perception uses. It sets a more significant proportion of computational resources on objects of interest and creates a more realistic visualization. As a supplementary contribution, A new volumetric point-cloud density-based Peak Signal-to-Noise Ratio (PSNR) metric is proposed to evaluate the introduced techniques. An in-depth evaluation of the presented systems, comparative examination of the proposed point cloud metric, user studies, and experiments demonstrated that the methods introduced in this thesis are visually superior while significantly reducing latency and throughput

Bioinspired template-based control of legged locomotion

cient and robust locomotion is a crucial condition for the more extensive use of legged robots in real world applications. In that respect, robots can learn from animals, if the principles underlying locomotion in biological legged systems can be transferred to their artificial counterparts. However, legged locomotion in biological systems is a complex and not fully understood problem. A great progress to simplify understanding locomotion dynamics and control was made by introducing simple models, coined ``templates'', able to represent the overall dynamics of animal (including human) gaits. One of the most recognized models is the spring-loaded inverted pendulum (SLIP) which consists of a point mass atop a massless spring. This model provides a good description of human gaits, such as walking, hopping and running. Despite its high level of abstraction, it supported and inspired the development of successful legged robots and was used as explicit targets for control, over the years. Inspired from template models explaining biological locomotory systems and Raibert's pioneering legged robots, locomotion can be realized by basic subfunctions: (i) stance leg function, (ii) leg swinging and (iii) balancing. Combinations of these three subfunctions can generate different gaits with diverse properties. Using the template models, we investigate how locomotor subfunctions contribute to stabilize different gaits (hopping, running and walking) in different conditions (e.g., speeds). We show that such basic analysis on human locomotion using conceptual models can result in developing new methods in design and control of legged systems like humanoid robots and assistive devices (exoskeletons, orthoses and prostheses). This thesis comprises research in different disciplines: biomechanics, robotics and control. These disciplines are required to do human experiments and data analysis, modeling of locomotory systems, and implementation on robots and an exoskeleton. We benefited from facilities and experiments performed in the Lauflabor locomotion laboratory. Modeling includes two categories: conceptual (template-based, e.g. SLIP) models and detailed models (with segmented legs, masses/inertias). Using the BioBiped series of robots (and the detailed BioBiped MBS models; MBS stands for Multi-Body-System), we have implemented newly-developed design and control methods related to the concept of locomotor subfunctions on either MBS models or on the robot directly. In addition, with involvement in BALANCE project (\url{http://balance-fp7.eu/}), we implemented balance-related control approaches on an exoskeleton to demonstrate their performance in human walking. The outcomes of this research includes developing new conceptual models of legged locomotion, analysis of human locomotion based on the newly developed models following the locomotor subfunction trilogy, developing methods to benefit from the models in design and control of robots and exoskeletons. The main contribution of this work is providing a novel approach for modular control of legged locomotion. With this approach we can identify the relation between different locomotor subfunctions e.g., between balance and stance (using stance force for tuning balance control) or balance and swing (two joint hip muscles can support the swing leg control relating it to the upper body posture) and implement the concept of modular control based on locomotor subfunctions with a limited exchange of sensory information on several hardware platforms (legged robots, exoskeleton)