39 research outputs found
Learning hybrid locomotion skills—Learn to exploit residual actions and modulate model-based gait control
This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking
Energy-Efficient Bipedal Gait Pattern Generation via CoM Acceleration Optimization
Energy consumption for bipedal walking plays a central role for a humanoid robot with limited battery capacity. Studies have revealed that exploiting the allowable Zero Moment Point region (AZR) and Center of Mass (CoM) height variation (CoMHV) are strategies capable of improving energy performance. In general, energetic cost is evaluated by integrating the electric power of multi joints. However, this Joint-Power-based Index requires computing joint torques and velocities in advance, which usually requires time-consuming iterative procedures, especially for multi-joints robots. In this work, we propose a CoM-Acceleration-based Optimal Index (CAOI) to synthesize an energetically efficient CoM trajectory. The proposed method is based on the Linear Inverted Pendulum Model, whose energetic cost can be easily measured by the input energy required for driving the point mass to track a reference trajectory. We characterize the CoM motion for a single walking cycle and define its energetic cost as Unit Energy Consumption. Based on the CAOI, an analytic solution for CoM trajectory generation is provided. Hardware experiments demonstrated the computational efficiency of the proposed approach and the energetic benefits of exploiting AZR and CoMHV strategies
Locomoção de humanoides robusta e versátil baseada em controlo analĂtico e fĂsica 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 robĂ´s humanoides sĂŁo 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 distĂşrbios 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 sĂŁo 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
analĂticas Ă© a chave para abrir as portas para robĂ´s humanoides
saĂrem dos laboratĂłrios. Propusemos um forte acoplamento entre controlo
analĂtico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analĂtico com mĂłdulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resĂduos
para ajustar as posições das juntas alvo do robĂ´ (fĂsica 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 ruĂdo e impulsos externos.Programa Doutoral em Informátic
From walking to running: robust and 3D humanoid gait generation via MPC
Humanoid robots are platforms that can succeed in tasks conceived for humans. From locomotion in unstructured environments, to driving cars, or working in industrial plants,
these robots have a potential that is yet to be disclosed in systematic every-day-life applications. Such a perspective, however, is opposed by the need of solving complex
engineering problems under the hardware and software point of view. In this thesis, we focus on the software side of the problem, and in particular on locomotion control. The operativity of a legged humanoid is subordinate to its capability of realizing a reliable locomotion. In many settings, perturbations may undermine the balance and make the robot fall. Moreover, complex and dynamic motions might be required by the context, as for instance it could be needed to start running or climbing stairs to achieve a certain location in the shortest time. We present gait generation schemes based on Model Predictive Control (MPC) that tackle both the problem of robustness and tridimensional dynamic motions. The proposed control schemes adopt the typical paradigm of centroidal MPC for reference motion generation, enforcing dynamic balance through the Zero Moment Point condition, plus a whole-body controller that maps the generated trajectories to joint commands. Each of the described predictive controllers also feature a so-called stability constraint, preventing the generation of diverging Center of Mass trajectories with respect to the Zero Moment Point. Robustness is addressed by modeling the humanoid as a Linear Inverted Pendulum and devising two types of strategies. For persistent perturbations, a way to use a disturbance observer and a technique for constraint tightening (to ensure robust constraint satisfaction) are presented. In the case of impulsive pushes instead, techniques for footstep and timing adaptation are introduced. The underlying approach is to interpret robustness as a MPC feasibility problem, thus aiming at ensuring the existence of a solution for the constrained optimization problem to be solved at each iteration in spite of the perturbations. This perspective allows to devise simple solutions to complex problems, favoring a reliable real-time implementation.
For the tridimensional locomotion, on the other hand, the humanoid is modeled as a Variable Height Inverted Pendulum. Based on it, a two stage MPC is introduced with particular emphasis on the implementation of the stability constraint. The overall result is a gait generation scheme that allows the robot to overcome relatively complex
environments constituted by a non-flat terrain, with also the capability of realizing running gaits. The proposed methods are validated in different settings: from conceptual simulations in Matlab to validations in the DART dynamic environment, up to experimental tests on the NAO and the OP3 platforms