633 research outputs found
Analytic Model for Quadruped Locomotion Task-Space Planning
Despite the extensive presence of the legged locomotion in animals, it is
extremely challenging to be reproduced with robots. Legged locomotion is an
dynamic task which benefits from a planning that takes advantage of the
gravitational pull on the system. However, the computational cost of such
optimization rapidly increases with the complexity of kinematic structures,
rendering impossible real-time deployment in unstructured environments. This
paper proposes a simplified method that can generate desired centre of mass and
feet trajectory for quadrupeds. The model describes a quadruped as two bipeds
connected via their centres of mass, and it is based on the extension of an
algebraic bipedal model that uses the topology of the gravitational attractor
to describe bipedal locomotion strategies. The results show that the model
generates trajectories that agrees with previous studies. The model will be
deployed in the future as seed solution for whole-body trajectory optimization
in the attempt to reduce the computational cost and obtain real-time planning
of complex action in challenging environments.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin German
Online Simultaneous Semi-Parametric Dynamics Model Learning
Accurate models of robots' dynamics are critical for control, stability,
motion optimization, and interaction. Semi-Parametric approaches to dynamics
learning combine physics-based Parametric models with unstructured
Non-Parametric regression with the hope to achieve both accuracy and
generalizablity. In this paper we highlight the non-stationary problem created
when attempting to adapt both Parametric and Non-Parametric components
simultaneously. We present a consistency transform designed to compensate for
this non-stationary effect, such that the contributions of both models can
adapt simultaneously without adversely affecting the performance of the
platform. Thus we are able to apply the Semi-Parametric learning approach for
continuous iterative online adaptation, without relying on batch or offline
updates. We validate the transform via a perfect virtual model as well as by
applying the overall system on a Kuka LWR IV manipulator. We demonstrate
improved tracking performance during online learning and show a clear
transference of contribution between the two components with a learning bias
towards the Parametric component.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
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this work in other work
Neural Lyapunov and Optimal Control
Optimal control (OC) is an effective approach to controlling complex
dynamical systems. However, traditional approaches to parameterising and
learning controllers in optimal control have been ad-hoc, collecting data and
fitting it to neural networks. However, this can lead to learnt controllers
ignoring constraints like optimality and time variability. We introduce a
unified framework that simultaneously solves control problems while learning
corresponding Lyapunov or value functions. Our method formulates OC-like
mathematical programs based on the Hamilton-Jacobi-Bellman (HJB) equation. We
leverage the HJB optimality constraint and its relaxation to learn time-varying
value and Lyapunov functions, implicitly ensuring the inclusion of constraints.
We show the effectiveness of our approach on linear and nonlinear
control-affine problems. Additionally, we demonstrate significant reductions in
planning horizons (up to a factor of 25) when incorporating the learnt
functions into Model Predictive Controllers
Adversarial Generation of Informative Trajectories for Dynamics System Identification
Dynamic System Identification approaches usually heavily rely on the
evolutionary and gradient-based optimisation techniques to produce optimal
excitation trajectories for determining the physical parameters of robot
platforms. Current optimisation techniques tend to generate single
trajectories. This is expensive, and intractable for longer trajectories, thus
limiting their efficacy for system identification. We propose to tackle this
issue by using multiple shorter cyclic trajectories, which can be generated in
parallel, and subsequently combined together to achieve the same effect as a
longer trajectory. Crucially, we show how to scale this approach even further
by increasing the generation speed and quality of the dataset through the use
of generative adversarial network (GAN) based architectures to produce a large
databases of valid and diverse excitation trajectories. To the best of our
knowledge, this is the first robotics work to explore system identification
with multiple cyclic trajectories and to develop GAN-based techniques for
scaleably producing excitation trajectories that are diverse in both control
parameter and inertial parameter spaces. We show that our approach dramatically
accelerates trajectory optimisation, while simultaneously providing more
accurate system identification than the conventional approach.Comment: Accepted for publication in IEEE iROS 202
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