47,702 research outputs found
Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking
In this paper, we propose an online learning approach that enables the
inverse dynamics model learned for a source robot to be transferred to a target
robot (e.g., from one quadrotor to another quadrotor with different mass or
aerodynamic properties). The goal is to leverage knowledge from the source
robot such that the target robot achieves high-accuracy trajectory tracking on
arbitrary trajectories from the first attempt with minimal data recollection
and training. Most existing approaches for multi-robot knowledge transfer are
based on post-analysis of datasets collected from both robots. In this work, we
study the feasibility of impromptu transfer of models across robots by learning
an error prediction module online. In particular, we analytically derive the
form of the mapping to be learned by the online module for exact tracking,
propose an approach for characterizing similarity between robots, and use these
results to analyze the stability of the overall system. The proposed approach
is illustrated in simulation and verified experimentally on two different
quadrotors performing impromptu trajectory tracking tasks, where the quadrotors
are required to accurately track arbitrary hand-drawn trajectories from the
first attempt.Comment: European Control Conference (ECC) 201
Minimax Iterative Dynamic Game: Application to Nonlinear Robot Control Tasks
Multistage decision policies provide useful control strategies in
high-dimensional state spaces, particularly in complex control tasks. However,
they exhibit weak performance guarantees in the presence of disturbance, model
mismatch, or model uncertainties. This brittleness limits their use in
high-risk scenarios. We present how to quantify the sensitivity of such
policies in order to inform of their robustness capacity. We also propose a
minimax iterative dynamic game framework for designing robust policies in the
presence of disturbance/uncertainties. We test the quantification hypothesis on
a carefully designed deep neural network policy; we then pose a minimax
iterative dynamic game (iDG) framework for improving policy robustness in the
presence of adversarial disturbances. We evaluate our iDG framework on a
mecanum-wheeled robot, whose goal is to find a ocally robust optimal multistage
policy that achieve a given goal-reaching task. The algorithm is simple and
adaptable for designing meta-learning/deep policies that are robust against
disturbances, model mismatch, or model uncertainties, up to a disturbance
bound. Videos of the results are on the author's website,
http://ecs.utdallas.edu/~opo140030/iros18/iros2018.html, while the codes for
reproducing our experiments are on github,
https://github.com/lakehanne/youbot/tree/rilqg. A self-contained environment
for reproducing our results is on docker,
https://hub.docker.com/r/lakehanne/youbotbuntu14/Comment: 2018 International Conference on Intelligent Robots and System
Iterative Application of the aiNET Algorithm in the Construction of a Radial Basis Function Neural Network
This paper presents some of the procedures adopted in the construction of a Radial Basis Function Neural Network by iteratively applying the aiNET, an Artificial Immune Systems Algorithm. These procedures have shown to be effective in terms of i) the free determination of centroids inspired by an immune heuristics; and ii) the achievement of appropriate minimal square errors after a number of iterations. Experimental and empirical results are compared aiming at confirming (or not) some hypotheses
Learning for Advanced Motion Control
Iterative Learning Control (ILC) can achieve perfect tracking performance for
mechatronic systems. The aim of this paper is to present an ILC design tutorial
for industrial mechatronic systems. First, a preliminary analysis reveals the
potential performance improvement of ILC prior to its actual implementation.
Second, a frequency domain approach is presented, where fast learning is
achieved through noncausal model inversion, and safe and robust learning is
achieved by employing a contraction mapping theorem in conjunction with
nonparametric frequency response functions. The approach is demonstrated on a
desktop printer. Finally, a detailed analysis of industrial motion systems
leads to several shortcomings that obstruct the widespread implementation of
ILC algorithms. An overview of recently developed algorithms, including
extensions using machine learning algorithms, is outlined that are aimed to
facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced
Motion Control, 202
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