18 research outputs found
Bipedal Robot Running: Human-like Actuation Timing Using Fast and Slow Adaptations
We have been developing human-sized biped robots based on passive dynamic
mechanisms. In human locomotion, the muscles activate at the same rate relative
to the gait cycle during running. To achieve adaptive running for robots, such
characteristics should be reproduced to yield the desired effect. In this
study, we designed a central pattern generator (CPG) involving fast and slow
adaptation to achieve human-like running using a simple spring-mass model and
our developed bipedal robot, which is equipped with actuators that imitate the
human musculoskeletal system. Our results demonstrate that fast and slow
adaptations can reproduce human-like running with a constant rate of muscle
firing relative to the gait cycle. Furthermore, the results suggest that the
CPG contributes to the adjustment of the muscle activation timing in human
running.Comment: 15 pages, 12 figures, submitted to Advanced Robotic
Adaptive quadruped locomotion: learning to detect and avoid an obstacle
Dissertação de mestrado em Engenharia de InformáticaAutonomy and adaptability are key features in the design and construction of a robotic
system capable of carrying out tasks in an unstructured and not predefined environment.
Such features are generally observed in animals, biological systems that usually serve as an
inspiration models to the design of robotic systems. The autonomy and adaptability of these
biological systems partially arises from their ability to learn. Animals learn to move and
control their own body when young, they learn to survive, to hunt and avoid undesirable
situations, from their progenitors.
There has been an increasing interest in defining a way to endow these abilities into
the design and creation of robotic systems. This dissertation proposes a mechanism that
seeks to create a learning module to a quadruped robot controller that enables it to both,
detect and avoid an obstacle in its path. The detection is based on a Forward Internal Model
(FIM) trained online to create expectations about the robot’s perceptive information. This
information is acquired by a set of range sensors that scan the ground in front of the robot in
order to detect the obstacle. In order to avoid stepping on the obstacle, the obstacle detections
are used to create a map of responses that will change the locomotion according to what is
necessary. The map is built and tuned every time the robot fails to step over the obstacle and
defines how the robot should act to avoid these situations in the future. Both learning tasks
are carried out online and kept active after the robot has learned, enabling the robot to adapt
to possible new situations.
The proposed architecture was inspired on [14, 17], but applied here to a quadruped robot
with different sensors and specific sensor configuration. Also, the mechanism is coupled
with the robot’s locomotion generator based in Central Pattern Generators (CPG)s presented
in [22]. In order to achieve its goal, the controller send commands to the CPG so that the
necessary changes in the locomotion are applied.
Results showed the success in both learning tasks. The robot was able to detect the
obstacle, and change its locomotion with the acquired information at collision time.Autonomia e capacidade de adaptação são características chave na criação de sistemas
robóticos capazes de levar a cabo diversas tarefas em ambientes não especificamente preparados
nem configurados para tal. Estas características são comuns nos animais, sistemas biológicos
que muitas vezes servem de modelo e inspiração para desenhar e construir sistemas
robóticos. A autonomia e adaptabilidade destes sistemas advém parcialmente da sua capacidade
de aprender. Quando ainda jovens, os animais aprendem a controlar o seu corpo e a
movimentar-se, muitos mamíferos aprendem a caçar e a sobreviver com os seus progenitores.
Ultimamente tem havido um crescente interesse em como aplicar estas características
no desenho e criação de sistemas robóticos. Nesta dissertação é proposto um mecanismo
que permita que um robô quadrúpede seja capaz de detectar e evitar um obstáculo no seu
caminho. A detecção é baseada num Forward Internal Model (FIM) que aprende a prever
os valores dos sensores de percepção do robô, os quais procuram detectar obstáculos no seu
caminho. Por forma a evitar os obstáculos, os sinais de detecção dos obstáculos são usados
na criação de um mapa que permitirá ao robô alterar a sua locomoção mediante o que é
necessário. Este mapa é construído à medida que o robô falha e tropeça no obstáculo. Nesse
momento, o mapa é definido para que tal situação seja evitada no futuro. Ambos os processos
de aprendizagem são levados a cabo em tempo de execução e mantêm esse processo activo
por forma a possibilitar a readaptação a eventuais novas situações.
Este mecanismo foi inspirado nos trabalhos [14, 17], mas aplicados aqui a um quadrúpede
com uma configuração de sensores diferente e específica. O mecanismo será interligado
ao gerador da locomoção, baseado em Control Pattern Generator (CPG) proposto em [22].
Por forma a atingir o objectivo final, o controlador irá enviar comandos para o CPG a fim da
locomoção ser alterada como necessário.
Os resultados obtidos mostram o sucesso em ambos os processos de aprendizagem. O
robô é capaz de detectar o obstáculo e alterar a sua locomção de acordo com a informação
adquirida nos momentos de colisão com o mesmo, conseguindo efectivamente passar por
cima do obstáculo sem nenhum tipo de colisão
Adaptive cancelation of self-generated sensory signals in a whisking robot
Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme
Behavior-specific proprioception models for robotic force estimation: a machine learning approach
Robots that support humans in physically demanding tasks require accurate force sensing capabilities. A common way to achieve this is by monitoring the interaction with the environment directly with dedicated force sensors. Major drawbacks of such special purpose sensors are the increased costs and the reduced payload of the robot platform. Instead, this thesis investigates how the functionality of such sensors can be approximated by utilizing force estimation approaches. Most of today’s robots are equipped with rich proprioceptive sensing capabilities where even a robotic arm, e.g., the UR5, provides access to more than hundred sensor readings. Following this trend, it is getting feasible to utilize a wide variety of sensors for force estimation purposes. Human proprioception allows estimating forces such as the weight of an object by prior experience about sensory-motor patterns. Applying a similar approach to robots enables them to learn from previous demonstrations without the need of dedicated force sensors.
This thesis introduces Behavior-Specific Proprioception Models (BSPMs), a novel concept for enhancing robotic behavior with estimates of the expected proprioceptive feedback. A main methodological contribution is the operationalization of the BSPM approach using data-driven machine learning techniques. During a training phase, the behavior is continuously executed while recording proprioceptive sensor readings. The training data acquired from these demonstrations represents ground truth about behavior-specific sensory-motor experiences, i.e., the influence of performed actions and environmental conditions on the proprioceptive feedback. This data acquisition procedure does not require expert knowledge about the particular robot platform, e.g., kinematic chains or mass distribution, which is a major advantage over analytical approaches. The training data is then used to learn BSPMs, e.g. using lazy learning techniques or artificial neural networks. At runtime, the BSPMs provide estimates of the proprioceptive feedback that can be compared to actual sensations. The BSPM approach thus extends classical programming by demonstrations methods where only movement data is learned and enables robots to accurately estimate forces during behavior execution
Generating walking behaviours in legged robots
Many legged robots have boon built with a variety of different abilities, from running
to liopping to climbing stairs. Despite this however, there has been no consistency of
approach to the problem of getting them to walk. Approaches have included breaking
down the walking step into discrete parts and then controlling them separately, using
springs and linkages to achieve a passive walking cycle, and even working out the
necessary movements in simulation and then imposing them on the real robot. All of
these have limitations, although most were successful at the task for which they were
designed. However, all of them fall into one of two categories: either they alter the
dynamics of the robots physically so that the robot, whilst very good at walking, is
not as general purpose as it once was (as with the passive robots), or they control the
physical mechanism of the robot directly to achieve their goals, and this is a difficult
task.In this thesis a design methodology is described for building controllers for 3D dynam¬
ically stable walking, inspired by the best walkers and runners around — ourselves —
so the controllers produced are based 011 the vertebrate Central Nervous System. This
means that there is a low-level controller which adapts itself to the robot so that, when
switched on, it can be considered to simulate the springs and linkages of the passive
robots to produce a walking robot, and this now active mechanism is then controlled
by a relatively simple higher level controller. This is the best of both worlds — we
have a robot which is inherently capable of walking, and thus is easy to control like
the passive walkers, but also retains the general purpose abilities which makes it so
potentially useful.This design methodology uses an evolutionary algorithm to generate low-level control¬
lers for a selection of simulated legged robots. The thesis also looks in detail at previous
walking robots and their controllers and shows that some approaches, including staged
evolution and hand-coding designs, may be unnecessary, and indeed inappropriate, at
least for a general purpose controller. The specific algorithm used is evolutionary, using
a simple genetic algorithm to allow adaptation to different robot configurations, and
the controllers evolved are continuous time neural networks. These are chosen because
of their ability to entrain to the movement of the robot, allowing the whole robot and
network to be considered as a single dynamical system, which can then be controlled
by a higher level system.An extensive program of experiments investigates the types of neural models and net¬
work structures which are best suited to this task, and it is shown that stateless and
simple dynamic neural models are significantly outperformed as controllers by more
complex, biologically plausible ones but that other ideas taken from biological systems,
including network connectivities, are not generally as useful and reasons for this are
examined.The thesis then shows that this system, although only developed 011 a single robot,
is capable of automatically generating controllers for a wide selection of different test
designs. Finally it shows that high level controllers, at least to control steering and
speed, can be easily built 011 top of this now active walking mechanism
Using evolutionary artificial neural networks to design hierarchical animat nervous systems.
The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any suitable technology, for many different environments. The implementation focused on the two lowest layers (the reflex and action layers) of the ANS, which are concerned with control and rhythmic movement. Both layers were realised as Artificial Neural Networks (ANN) which were created using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the position of an actuator (such as linear actuators or D.C. motors). The action layer performed the task of Central Pattern Generators (CPG), which produce rhythmic patterns of activity. In particular, different biped and quadruped gait patterns were created. An original neural model was specifically developed for assisting in the creation of these time-based patterns. It is shown in the thesis that Artificial Reflexes and CPGs can be configured successfully using this technique. The Artificial Reflexes were better at generalising across different actuators, without changes, than traditional controllers. Gaits such as pace, trot, gallop and pronk were successfully created using the CPGs. Experiments were conducted to determine whether modularity in the networks had an impact. It has been demonstrated that the degree of modularization in the network influences its evolvability, with more modular networks evolving more efficiently