9,614 research outputs found
Adaptive Control of 4-DoF Robot manipulator
In experimental robotics, researchers may face uncertainties in parameters of
a robot manipulator that they are working with. This uncertainty may be caused
by deviations in the manufacturing process of a manipulator, or changes applied
to manipulator in the lab for sake of experiments. Another situation when
dynamical and inertial parameters of a robot are uncertain arises, is the
grasping of objects by a manipulator. In all these situations there is a need
for adaptive control strategies that would identify changes in dynamical
properties of manipulator and adjust for them. This article presents a work on
designing of an adaptive control strategy for 4-DoF manipulator with uncertain
dynamical properties, and outcomes of testing of this strategy applied to
control of simulator of robot.Comment: 7 pages, 4(5) figure
Rigid vs compliant contact: An experimental study on biped walking
Contact modeling plays a central role in motion planning, simulation, and
control of legged robots, as legged locomotion is realized through contact. The
two prevailing approaches to model the contact consider rigid and compliant
premise at interaction ports. Contrary to the dynamics model of legged systems
with rigid contact (without impact) which is straightforward to develop, there
is no consensus among researchers to employ a standard compliant contact model.
Our main goal in this paper is to study the dynamics model structure of bipedal
walking systems with a rigid contact and a \textit{novel} compliant contact
model and to present experimental validation of both models. For the model with
rigid contact, after developing the model of the articulated bodies in flight
phase without any contact with the environment, we apply the holonomic
constraints at contact points and develop a constrained dynamics model of the
robot in both single and double support phases. For the model with compliant
contact, we propose a novel nonlinear contact model and simulate the motion of
the robot using this model. In order to show the performance of the developed
models, we compare obtained results from these models to the empirical
measurements from bipedal walking of the human-sized humanoid robot SURENA III,
which has been designed and fabricated at CAST, University of Tehran. This
analysis shows the merit of both models in estimating dynamic behavior of the
robot walking on a semi-rigid surface. The model with rigid contact, which is
less complex and independent of the physical properties of the contacting
bodies, can be employed for model-based motion optimization, analysis as well
as control, while the model with compliant contact and more complexity is
suitable for more realistic simulation scenarios
A Tour of Reinforcement Learning: The View from Continuous Control
This manuscript surveys reinforcement learning from the perspective of
optimization and control with a focus on continuous control applications. It
surveys the general formulation, terminology, and typical experimental
implementations of reinforcement learning and reviews competing solution
paradigms. In order to compare the relative merits of various techniques, this
survey presents a case study of the Linear Quadratic Regulator (LQR) with
unknown dynamics, perhaps the simplest and best-studied problem in optimal
control. The manuscript describes how merging techniques from learning theory
and control can provide non-asymptotic characterizations of LQR performance and
shows that these characterizations tend to match experimental behavior. In
turn, when revisiting more complex applications, many of the observed phenomena
in LQR persist. In particular, theory and experiment demonstrate the role and
importance of models and the cost of generality in reinforcement learning
algorithms. This survey concludes with a discussion of some of the challenges
in designing learning systems that safely and reliably interact with complex
and uncertain environments and how tools from reinforcement learning and
control might be combined to approach these challenges.Comment: minor revision with a few clarifying passages and corrected typo
Learning Agile Robotic Locomotion Skills by Imitating Animals
Reproducing the diverse and agile locomotion skills of animals has been a
longstanding challenge in robotics. While manually-designed controllers have
been able to emulate many complex behaviors, building such controllers involves
a time-consuming and difficult development process, often requiring substantial
expertise of the nuances of each skill. Reinforcement learning provides an
appealing alternative for automating the manual effort involved in the
development of controllers. However, designing learning objectives that elicit
the desired behaviors from an agent can also require a great deal of
skill-specific expertise. In this work, we present an imitation learning system
that enables legged robots to learn agile locomotion skills by imitating
real-world animals. We show that by leveraging reference motion data, a single
learning-based approach is able to automatically synthesize controllers for a
diverse repertoire behaviors for legged robots. By incorporating sample
efficient domain adaptation techniques into the training process, our system is
able to learn adaptive policies in simulation that can then be quickly adapted
for real-world deployment. To demonstrate the effectiveness of our system, we
train an 18-DoF quadruped robot to perform a variety of agile behaviors ranging
from different locomotion gaits to dynamic hops and turns
Dynamically Stable 3D Quadrupedal Walking with Multi-Domain Hybrid System Models and Virtual Constraint Controllers
Hybrid systems theory has become a powerful approach for designing feedback
controllers that achieve dynamically stable bipedal locomotion, both formally
and in practice. This paper presents an analytical framework 1) to address
multi-domain hybrid models of quadruped robots with high degrees of freedom,
and 2) to systematically design nonlinear controllers that asymptotically
stabilize periodic orbits of these sophisticated models. A family of
parameterized virtual constraint controllers is proposed for continuous-time
domains of quadruped locomotion to regulate holonomic and nonholonomic outputs.
The properties of the Poincare return map for the full-order and closed-loop
hybrid system are studied to investigate the asymptotic stabilization problem
of dynamic gaits. An iterative optimization algorithm involving linear and
bilinear matrix inequalities is then employed to choose stabilizing virtual
constraint parameters. The paper numerically evaluates the analytical results
on a simulation model of an advanced 3D quadruped robot, called GR Vision 60,
with 36 state variables and 12 control inputs. An optimal amble gait of the
robot is designed utilizing the FROST toolkit. The power of the analytical
framework is finally illustrated through designing a set of stabilizing virtual
constraint controllers with 180 controller parameters.Comment: American Control Conference 201
Safe Robotic Grasping: Minimum Impact-Force Grasp Selection
This paper addresses the problem of selecting from a choice of possible
grasps, so that impact forces will be minimised if a collision occurs while the
robot is moving the grasped object along a post-grasp trajectory. Such
considerations are important for safety in human-robot interaction, where even
a certified "human-safe" (e.g. compliant) arm may become hazardous once it
grasps and begins moving an object, which may have significant mass, sharp
edges or other dangers. Additionally, minimising collision forces is critical
to preserving the longevity of robots which operate in uncertain and hazardous
environments, e.g. robots deployed for nuclear decommissioning, where removing
a damaged robot from a contaminated zone for repairs may be extremely difficult
and costly. Also, unwanted collisions between a robot and critical
infrastructure (e.g. pipework) in such high-consequence environments can be
disastrous. In this paper, we investigate how the safety of the post-grasp
motion can be considered during the pre-grasp approach phase, so that the
selected grasp is optimal in terms applying minimum impact forces if a
collision occurs during a desired post-grasp manipulation. We build on the
methods of augmented robot-object dynamics models and "effective mass" and
propose a method for combining these concepts with modern grasp and trajectory
planners, to enable the robot to achieve a grasp which maximises the safety of
the post-grasp trajectory, by minimising potential collision forces. We
demonstrate the effectiveness of our approach through several experiments with
both simulated and real robots.Comment: To be appeared in IEEE/RAS IROS 201
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Although reinforcement learning methods can achieve impressive results in
simulation, the real world presents two major challenges: generating samples is
exceedingly expensive, and unexpected perturbations or unseen situations cause
proficient but specialized policies to fail at test time. Given that it is
impractical to train separate policies to accommodate all situations the agent
may see in the real world, this work proposes to learn how to quickly and
effectively adapt online to new tasks. To enable sample-efficient learning, we
consider learning online adaptation in the context of model-based reinforcement
learning. Our approach uses meta-learning to train a dynamics model prior such
that, when combined with recent data, this prior can be rapidly adapted to the
local context. Our experiments demonstrate online adaptation for continuous
control tasks on both simulated and real-world agents. We first show simulated
agents adapting their behavior online to novel terrains, crippled body parts,
and highly-dynamic environments. We also illustrate the importance of
incorporating online adaptation into autonomous agents that operate in the real
world by applying our method to a real dynamic legged millirobot. We
demonstrate the agent's learned ability to quickly adapt online to a missing
leg, adjust to novel terrains and slopes, account for miscalibration or errors
in pose estimation, and compensate for pulling payloads.Comment: First 2 authors contributed equally. Website:
https://sites.google.com/berkeley.edu/metaadaptivecontro
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
We introduce Embed to Control (E2C), a method for model learning and control
of non-linear dynamical systems from raw pixel images. E2C consists of a deep
generative model, belonging to the family of variational autoencoders, that
learns to generate image trajectories from a latent space in which the dynamics
is constrained to be locally linear. Our model is derived directly from an
optimal control formulation in latent space, supports long-term prediction of
image sequences and exhibits strong performance on a variety of complex control
problems.Comment: Final NIPS versio
Experimental comparison of control strategies for trajectory tracking for mobile robots
The purpose of this paper is to implement, test and compare the performance of different control strategies for tracking trajectory for mobile robots. The control strategies used are based on linear algebra, PID controller and on a sliding mode controller. Each control scheme is developed taking into consideration the model of the robot. The linear algebra approaches take into account the complete kinematic model of the robot; and the PID and the sliding mode controller use a reduced order model, which is obtained considering the mobile robot platform as a black-box. All the controllers are tested and compared, firstly by simulations and then, by using a Pioneer 3DX robot in field experiments.Fil: Capito, Linda. Escuela PolitĂ©cnica Nacional; EcuadorFil: Proaño, Pablo. Escuela PolitĂ©cnica Nacional; EcuadorFil: Camacho, Oscar. Escuela PolitĂ©cnica Nacional; EcuadorFil: Rosales, AndrĂ©s. Escuela PolitĂ©cnica Nacional; EcuadorFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de IngenierĂa QuĂmica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan; Argentin
Mathematical Analysis of Multi-Agent Systems
We review existing approaches to mathematical modeling and analysis of
multi-agent systems in which complex collective behavior arises out of local
interactions between many simple agents. Though the behavior of an individual
agent can be considered to be stochastic and unpredictable, the collective
behavior of such systems can have a simple probabilistic description. We show
that a class of mathematical models that describe the dynamics of collective
behavior of multi-agent systems can be written down from the details of the
individual agent controller. The models are valid for Markov or memoryless
agents, in which each agents future state depends only on its present state and
not any of the past states. We illustrate the approach by analyzing in detail
applications from the robotics domain: collaboration and foraging in groups of
robots.Comment: latex, 15 figures, 42 page
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