dmpbbo-master
所属分类:人工智能/神经网络/深度学习
开发工具:C++ Builder
文件大小:2865KB
下载次数:2
上传日期:2016-09-19 07:02:44
上 传 者:
yiyang0517
说明: 机器人DMP black-box optimization 算法
(robot dynamic movement prelimitry and black box optimization algorithm)
文件列表:
.todo.txt (1229, 2015-07-30)
.travis.yml (2355, 2015-07-30)
CMakeLists.txt (3402, 2015-07-30)
INSTALL.txt (5379, 2015-07-30)
LICENSE.txt (15220, 2015-07-30)
LICENSE_LESSER.txt (24487, 2015-07-30)
THANKS.txt (509, 2015-07-30)
docs (0, 2015-07-30)
docs\Doxyfile.in (72560, 2015-07-30)
docs\dmp.bib (1837, 2015-07-30)
docs\doxygen_custom.css (7911, 2015-07-30)
docs\eigenrealtime (0, 2015-07-30)
docs\eigenrealtime\Makefile (203, 2015-07-30)
docs\eigenrealtime\eigenrealtime.md (10045, 2015-07-30)
docs\eigenrealtime\gaussian.cpp (2060, 2015-07-30)
docs\eigenrealtime\nonrealtime.cpp (691, 2015-07-30)
docs\eigenrealtime\realtime.cpp (1106, 2015-07-30)
docs\eigenrealtime\realtimechecks.hpp (813, 2015-07-30)
docs\images (0, 2015-07-30)
docs\images\change_tau_attr-svg.pdf (39176, 2015-07-30)
docs\images\change_tau_attr-svg.png (36895, 2015-07-30)
docs\images\change_tau_attr.svg (105273, 2015-07-30)
docs\images\dmp-svg.pdf (177219, 2015-07-30)
docs\images\dmp-svg.png (130139, 2015-07-30)
docs\images\dmp.svg (211845, 2015-07-30)
docs\images\dmp_and_goal_system-svg.pdf (84133, 2015-07-30)
docs\images\dmp_and_goal_system-svg.png (66355, 2015-07-30)
docs\images\dmp_and_goal_system.svg (82444, 2015-07-30)
docs\images\dmp_forcing_terms-svg.pdf (53984, 2015-07-30)
docs\images\dmp_forcing_terms-svg.png (52591, 2015-07-30)
docs\images\dmp_forcing_terms.svg (85849, 2015-07-30)
docs\images\dmpplot_ijspeert2002movement-svg.pdf (145673, 2015-07-30)
docs\images\dmpplot_ijspeert2002movement-svg.png (87404, 2015-07-30)
docs\images\dmpplot_ijspeert2002movement.svg (544657, 2015-07-30)
... ...
# What?
This repository provides an implementation of dynamical systems,
function approximators,
[dynamical movement primitives](http://www-clmc.usc.edu/Resources/Details?id=2663), and black-box optimization
with evolution strategies, in particular the optimization of the parameters
of dynamical movement primitives.
A PDF tutorial on these topics (generated with Doxygen) is available at https://github.com/stulp/dmpbbo/blob/master/docs/tutorial.pdf
The complete HTML doxygen documentation is at: http://perso.ensta-paristech.fr/~stulp/dmpbbo/
# For whom?
This library may be useful for you if you
+ are new to dynamical movement primitives and want to learn about them (see the
tutorial in the doxygen documentation).
+ already know about dynamical movement primitives, but would rather use existing,
tested code than brew it yourself.
+ want to do reinforcement learning/optimization of dynamical movement primitives.
Most submodules of this project are independent of all others, so if you don't care
about dynamical movement primitives, the following submodules can still easily be
integrated in other code to perform some (hopefully) useful function:
+ `functionapproximators/` : a module that defines a generic interface for function
approximators, as well as several specific implementations (LWR, LWPR, iRFRLS, GMR)
+ `dynamicalsystems/` : a module that defines a generic interface for dynamical
systems, as well as several specific implementations (exponential, sigmoid,
spring-damper)
+ `bbo/` : implementation of some (rather simple) algorithms for the stochastic
optimization of black-box cost functions
If you use this library in the context of experiments for a scientific paper, we would appreciate if you could cite this library in the paper as follows:
@MISC{stulp_dmpbbo,
author = {Freek Stulp},
title = {{\tt DmpBbo} -- A C++ library for black-box optimization of
dynamical movement primitives.},
year = {2014},
url = {https://github.com/stulp/dmpbbo.git}
}
# How?
How to install the libraries/binaries/documentation is described in INSTALL.txt
To learn how to use the code, the first thing to do is look at the
documentation and tutorial here:
+ `build_dir/docs/html/index.html` This documentation must first be generated with doxygen, see INSTALL.txt
+ `docs/tutorial.pdf` This is a snapshot of the PDF in docs/tutorial/
To delve into the code a bit deeper, each module has a set of demos, e.g.
+ `src/dynamicalsystems/demos/`
The demos do not show all the functionality, but are well
documented and a good place to understand how the code can be
used. There are python scripts that call the right executables, and
do some plotting.
For more advanced stuff, you can also have a look at the tests, e.g.
+ `src/dynamicalsystems/tests/`
These are not unit tests per se, but more debugging tools that visualize the
results of an experiment or parameter setting. The tests
are not well documented, but exploit more of the functionality of
the code. Note that the test binaries are only built in debug
mode (in bin_test)
# Why?
For our own use, the aims of coding this were the following:
+ Allowing easy and modular exchange of different dynamical systems within
dynamical movement primitives.
+ Allowing easy and modular exchange of different function approximators within
dynamical movement primitives.
+ Being able to compare different exploration strategies (e.g. covariance matrix
adaptation vs. exploration decay) when optimizing dynamical movement primitives.
+ Enabling the optimization of different parameter subsets of function approximators.
+ Running dynamical movement primitives on real robots.
### Build Status
[![Build Status](https://travis-ci.org/stulp/dmpbbo.svg?branch=master)](https://travis-ci.org/stulp/dmpbbo)
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