Reinforcement Learning of Stable Trajectory for Quasi-Passive Dynamic Walking of an Unstable Biped Robot

Biped walking is one of the major research targets in recent humanoid robotics, and many researchers are now interested in Passive Dynamic Walking (PDW) [McGeer (1990)] rather than that by the conventional Zero Moment Point (ZMP) criterion [Vukobratovic (1972)]. The ZMP criterion is usually used for planning a desired trajectory to be tracked by

Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks

The complexity of bipedal locomotion may be attributed to the difficulty in synchronizing joint movements while at the same time achieving high-level objectives such as walking in a particular direction. Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion. However, most existing CPG-based approaches do not address the problem of high-level control explicitly. We propose a novel hierarchical control mechanism for bipedal locomotion where an optimized CPG network is used for joint control and a neural network acts as a high-level controller for modulating the CPG network. By separating motion generation from motion modulation, the high-level controller does not need to control individual joints directly but instead can develop to achieve a higher goal using a low-dimensional control signal. The feasibility of the hierarchical controller is demonstrated through simulation experiments using the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the controller's ability to function even without the availability of an exact robot model.Comment: In: Proceedings of the Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo, Norway, Aug. 19-22, 201

Biped dynamic walking using reinforcement learning

This thesis presents a study of biped dynamic walking using reinforcement learning. A hardware biped robot was built. It uses low gear ratio DC motors in order to provide free leg movements. The Self Scaling Reinforcement learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. A new learning architecture was designed to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of modules that represent new knowledge, or new requirements for the desired task. Control experiments were carried out using a simulator and the physical biped. The biped learned dynamic walking on flat surfaces without any previous knowledge about its dynamic model

Locomoção de humanoides robusta e versátil baseada em controlo analítico e física residual

Humanoid robots are made to resemble humans but their locomotion abilities are far from ours in terms of agility and versatility. When humans walk on complex terrains or face external disturbances, they combine a set of strategies, unconsciously and efficiently, to regain stability. This thesis tackles the problem of developing a robust omnidirectional walking framework, which is able to generate versatile and agile locomotion on complex terrains. We designed and developed model-based and model-free walk engines and formulated the controllers using different approaches including classical and optimal control schemes and validated their performance through simulations and experiments. These frameworks have hierarchical structures that are composed of several layers. These layers are composed of several modules that are connected together to fade the complexity and increase the flexibility of the proposed frameworks. Additionally, they can be easily and quickly deployed on different platforms. Besides, we believe that using machine learning on top of analytical approaches is a key to open doors for humanoid robots to step out of laboratories. We proposed a tight coupling between analytical control and deep reinforcement learning. We augmented our analytical controller with reinforcement learning modules to learn how to regulate the walk engine parameters (planners and controllers) adaptively and generate residuals to adjust the robot’s target joint positions (residual physics). The effectiveness of the proposed frameworks was demonstrated and evaluated across a set of challenging simulation scenarios. The robot was able to generalize what it learned in one scenario, by displaying human-like locomotion skills in unforeseen circumstances, even in the presence of noise and external pushes.Os robôs humanoides são feitos para se parecerem com humanos, mas suas habilidades de locomoção estão longe das nossas em termos de agilidade e versatilidade. Quando os humanos caminham em terrenos complexos ou enfrentam distúrbios externos combinam diferentes estratégias, de forma inconsciente e eficiente, para recuperar a estabilidade. Esta tese aborda o problema de desenvolver um sistema robusto para andar de forma omnidirecional, capaz de gerar uma locomoção para robôs humanoides versátil e ágil em terrenos complexos. Projetámos e desenvolvemos motores de locomoção sem modelos e baseados em modelos. Formulámos os controladores usando diferentes abordagens, incluindo esquemas de controlo clássicos e ideais, e validámos o seu desempenho por meio de simulações e experiências reais. Estes frameworks têm estruturas hierárquicas compostas por várias camadas. Essas camadas são compostas por vários módulos que são conectados entre si para diminuir a complexidade e aumentar a flexibilidade dos frameworks propostos. Adicionalmente, o sistema pode ser implementado em diferentes plataformas de forma fácil. Acreditamos que o uso de aprendizagem automática sobre abordagens analíticas é a chave para abrir as portas para robôs humanoides saírem dos laboratórios. Propusemos um forte acoplamento entre controlo analítico e aprendizagem profunda por reforço. Expandimos o nosso controlador analítico com módulos de aprendizagem por reforço para aprender como regular os parâmetros do motor de caminhada (planeadores e controladores) de forma adaptativa e gerar resíduos para ajustar as posições das juntas alvo do robô (física residual). A eficácia das estruturas propostas foi demonstrada e avaliada em um conjunto de cenários de simulação desafiadores. O robô foi capaz de generalizar o que aprendeu em um cenário, exibindo habilidades de locomoção humanas em circunstâncias imprevistas, mesmo na presença de ruído e impulsos externos.Programa Doutoral em Informátic

Fast biped walking with a neuronal controller and physical computation

Biped walking remains a difficult problem and robot models can greatly {facilitate} our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network, which is governed mainly by local sensor signals. This study shows that human-like gaits emerge without {specific} position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (AEA=Anterior Extreme Angle and GC=Ground Contact) which operate at the inter-joint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motorneurons in our reflexive controller are directly driving the motors of the joints, rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuro-mechanical system and this study emphasises that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using two real robot as well as by a Poincar\'{e} map analysis applied on a model of the robot in order to assess its stability. In addition, this neuronal control structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the neurons in real-time, during walking. This way the robot can reach a record-breaking walking speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is even comparable to the fastest relative speed of human walking