672 research outputs found
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement
There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Final results demonstrate that optimization is a valid gait planning technique.This work was been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the project PEst-OE/EEI/UI0319/2014.info:eu-repo/semantics/publishedVersio
Locomoção de humanoides robusta e versátil baseada em controlo analÃtico e fÃsica 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
analÃticas é a chave para abrir as portas para robôs humanoides
saÃrem dos laboratórios. Propusemos um forte acoplamento entre controlo
analÃtico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analÃtico com módulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resÃduos
para ajustar as posições das juntas alvo do robô (fÃsica 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 ruÃdo e impulsos externos.Programa Doutoral em Informátic
Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics
Selecting robot design parameters can be challenging since these parameters
are often coupled with the performance of the controller and, therefore, the
resulting capabilities of the robot. This leads to a time-consuming and often
expensive process whereby one iterates between designing the robot and manually
evaluating its capabilities. This is particularly challenging for bipedal
robots, where it can be difficult to evaluate the behavior of the system due to
the underlying nonlinear and hybrid dynamics. Thus, in an effort to streamline
the design process of bipedal robots, and maximize their performance, this
paper presents a systematic framework for the co-design of humanoid robots and
their associated walking gaits. To this end, we leverage the framework of
hybrid zero dynamic (HZD) gait generation, which gives a formal approach to the
generation of dynamic walking gaits. The key novelty of this paper is to
consider both virtual constraints associated with the actuators of the robot,
coupled with design virtual constraints that encode the associated parameters
of the robot to be designed. These virtual constraints are combined in an HZD
optimization problem which simultaneously determines the design parameters
while finding a stable walking gait that minimizes a given cost function. The
proposed approach is demonstrated through the design of a novel humanoid robot,
ADAM, wherein its thigh and shin are co-designed so as to yield energy
efficient bipedal locomotion.Comment: 7 pages, 6 figures, accepted to CDC 202
Reachability Map for Diverse and Energy Efficient Stepping of Humanoids
In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot be captured by simple models. This work studies the totality of robot dynamics and whole-body optimal control as a coupled system to investigate energy consumption during balance recovery. We developed a two-phase nonlinear optimization pipeline for dynamic stepping, which generates reachability maps showing complex energy-stepping relations. We optimize gait parameters to search all reachable locations and quantify the energy cost during dynamic transitions, which allows studying the relationship between energy consumption and stepping locations given different initial conditions. We found that to achieve efficient actuation, the stepping location and timing can have simple approximations close to the underlying optimality, resulting in optimal step positions with a 10.9% lower energy cost than those generated by linear inverted pendulum model. Despite the complexity of this nonlinear process, we found that near-minimal effort stepping locations are within a region of attractions, rather than a narrow solution space suggested by a simple model. This provides new insights into the nonuniqueness of near-optimal solutions in robot motion planning and control, and the diversity of stepping behavior in humans
Recommended from our members
Control Implementation of Dynamic Locomotion on Compliant, Underactuated, Force-Controlled Legged Robots with Non-Anthropomorphic Design
The control of locomotion on legged robots traditionally involves a robot that takes a standard legged form, such as the anthropomorphic humanoid, the dog-like quadruped, or the bird-like biped. Additionally, these systems will often be actuated with position-controlled servos or series-elastic actuators that are connected through rigid links. This work investigates the control implementation of dynamic, force-controlled locomotion on a family of legged systems that significantly deviate from these classic paradigms by incorporating modern, state-of-the-art proprioceptive actuators on uniquely configured compliant legs that do not closely resemble those found in nature. The results of this work can be used to better inform how to implement controllers on legged systems without stiff, position-controlled actuators, and also provide insight on how intelligently designed mechanical features can potentially simplify the control of complex, nonlinear dynamical systems like legged robots. To this end, this work presents the approach to control for a family of non-anthropomorphic bipedal robotic systems which are developed both in simulation and with physical hardware. The first is the Non-Anthropomorphic Biped, Version 1 (NABi-1) that features position-controlled joints along with a compliant foot element on a minimally actuated leg, and is controlled using simple open-loop trajectories based on the Zero Moment Point. The second system is the second version of the non-anthropomorphic biped (NABi-2) which utilizes the proprioceptive Back-drivable Electromagnetic Actuator for Robotics (BEAR) modules for actuation and fully realizes feedback-based force controlled locomotion. These systems are used to highlight both the strengths and weaknesses of utilizing proprioceptive actuation in systems, and suggest the tradeoffs that are made when using force control for dynamic locomotion. These systems also present case studies for different approaches to system design when it comes to bipedal legged robots
A passive system approach to increase the energy efficiency in walk movements based in a realistic simulation environment
This paper presents a passive system that increases the walk energy efficiency of a Humanoid robot. A passive system is applied to the simulated robot allowing the energy consumption to be reduced. The optimal parameters for the passive system depend on the joint and gait trajectories. Final results prove the benefits of the presented system apply. It was optimized thanks to a realistic simulator where the humanoid robot was modeled. The model was validated against a real robot
- …