36 research outputs found
Identification of Fully Physical Consistent Inertial Parameters using Optimization on Manifolds
This paper presents a new condition, the fully physical consistency for a set
of inertial parameters to determine if they can be generated by a physical
rigid body. The proposed condition ensure both the positive definiteness and
the triangular inequality of 3D inertia matrices as opposed to existing
techniques in which the triangular inequality constraint is ignored. This paper
presents also a new parametrization that naturally ensures that the inertial
parameters are fully physical consistency. The proposed parametrization is
exploited to reformulate the inertial identification problem as a manifold
optimization problem, that ensures that the identified parameters can always be
generated by a physical body. The proposed optimization problem has been
validated with a set of experiments on the iCub humanoid robot.Comment: 6 pages, published in Intelligent Robots and Systems (IROS), 2016
IEEE/RSJ International Conference o
On Centroidal Dynamics and Integrability of Average Angular Velocity
In the literature on robotics and multibody dynamics, the concept of average
angular velocity has received considerable attention in recent years. We
address the question of whether the average angular velocity defines an
orientation framethat depends only on the current robot configuration and
provide a simple algebraic condition to check whether this holds. In the
language of geometric mechanics, this condition corresponds to requiring the
flatness of the mechanical connection associated to the robotic system. Here,
however, we provide both a reinterpretation and a proof of this result
accessible to readers with a background in rigid body kinematics and multibody
dynamics but not necessarily acquainted with differential geometry, still
providing precise links to the geometric mechanics literature. This should help
spreading the algebraic condition beyond the scope of geometric
mechanics,contributing to a proper utilization and understanding of the concept
of average angular velocity.Comment: 8 pages, accepted for IEEE Robotics and Automation Letters (RA-L
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in
robotics. Several model classes including rigid body dynamics (RBD) models,
data-driven models and semiparametric models (which are a combination of the
previous two classes) are placed in a common framework. While model classes
used in the literature typically exploit joint velocities and accelerations,
which need to be approximated resorting to numerical differentiation schemes,
in this paper a new `derivative-free' framework is proposed that does not
require this preprocessing step. An extensive experimental study with real data
from the right arm of the iCub robot is presented, comparing different model
classes and estimation procedures, showing that the proposed `derivative-free'
methods outperform existing methodologies.Comment: 14 pages, 11 figure
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning
This paper presents Gym-Ignition, a new framework to create reproducible
robotic environments for reinforcement learning research. It interfaces with
the new generation of Gazebo, part of the Ignition Robotics suite, which
provides three main improvements for reinforcement learning applications
compared to the alternatives: 1) the modular architecture enables using the
simulator as a C++ library, simplifying the interconnection with external
software; 2) multiple physics and rendering engines are supported as plugins,
simplifying their selection during the execution; 3) the new distributed
simulation capability allows simulating complex scenarios while sharing the
load on multiple workers and machines. The core of Gym-Ignition is a component
that contains the Ignition Gazebo simulator and exposes a simple interface for
its configuration and execution. We provide a Python package that allows
developers to create robotic environments simulated in Ignition Gazebo.
Environments expose the common OpenAI Gym interface, making them compatible
out-of-the-box with third-party frameworks containing reinforcement learning
algorithms. Simulations can be executed in both headless and GUI mode, the
physics engine can run in accelerated mode, and instances can be parallelized.
Furthermore, the Gym-Ignition software architecture provides abstraction of the
Robot and the Task, making environments agnostic on the specific runtime. This
abstraction allows their execution also in a real-time setting on actual
robotic platforms, even if driven by different middlewares.Comment: Accepted in SII202
Efficient Geometric Linearization of Moving-Base Rigid Robot Dynamics
The linearization of the equations of motion of a robotics system about a
given state-input trajectory, including a controlled equilibrium state, is a
valuable tool for model-based planning, closed-loop control, gain tuning, and
state estimation. Contrary to the case of fixed based manipulators with
prismatic or rotary joints, the state space of moving-base robotic systems such
as humanoids, quadruped robots, or aerial manipulators cannot be globally
parametrized by a finite number of independent coordinates. This impossibility
is a direct consequence of the fact that the state of these systems includes
the system's global orientation, formally described as an element of the
special orthogonal group SO(3). As a consequence, obtaining the linearization
of the equations of motion for these systems is typically resolved, from a
practical perspective, by locally parameterizing the system's attitude by means
of, e.g., Euler or Cardan angles. This has the drawback, however, of
introducing artificial parameterization singularities and extra derivative
computations. In this contribution, we show that it is actually possible to
define a notion of linearization that does not require the use of a local
parameterization for the system's orientation, obtaining a mathematically
elegant, recursive, and singularity-free linearization for moving-based robot
systems. Recursiveness, in particular, is obtained by proposing a nontrivial
modification of existing recursive algorithms to allow for computations of the
geometric derivatives of the inverse dynamics and the inverse of the mass
matrix of the robotic system. The correctness of the proposed algorithm is
validated by means of a numerical comparison with the result obtained via
geometric finite difference