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ALR SimulationFramework

Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.

The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda

We currently support the following physic engines, which can be used interchangebly:

Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.

System Requirements

This simulation framework has been tested in the following operating systems:

  • Ubuntu 18.04, 20.04
  • Mac OS

Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here

Installation

Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide

Style guidelines

Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before each commit, all necessary style guides are checked and, if possible, automatically corrected.

Quickstart Guide

We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts. Please refer the Guide Chapters marked as [recommended].

Helpful Resources

Physic engines:

Gyms & Reinforcement Learning:

About

A stable and public version of SimulationFramework for KIT Robot Praktikum usage

Resources

Readme
MIT license
Activity
Custom properties

Stars

7 stars

Watchers

4 watching

Forks

10 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
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ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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ALR SimulationFramework
Maintained by the ALR team at KIT, this Python framework can be used to simulate robotic tasks for scientific research and teaching. Our default environment consists of a Franka Panda robot model.
The Franka Panda robot, is a robotic arm with 7 degrees of freedom, 1KHz control, torque-sensing in all joints, access to multiple research apps and full ROS capability. Franka Panda is therefore widely used in scientific research, including robot manipulation, machine learning etc. For more information, please visit: Franka Panda
We currently support the following physic engines, which can be used interchangebly:
Additionally we provide some environments optimized for reinforcement learning. They follow the OpenAI Gym interface and support the deployment of Stable Baselines models.
System Requirements
This simulation framework has been tested in the following operating systems:
  • Ubuntu 18.04, 20.04
  • Mac OS
Ubuntu 22.04 currently only works in Xorg Mode, for a small tutorial on how to switch to Xorg from Wayland look here
Installation
Due to the various dependencies of the physics engines, we recommend installing the ALR Simulationframework in a Anaconda/Miniconda environment. Please follow the steps as described in our detailed Installation Guide
Style guidelines
Please make sure that your code conforms to flake8 conventions. You can automatically check and correct all requirements by using
pre-commit. Follow the installation instructions and run pre-commit run --all-files to verify your installation. Before
each commit, all necessary style guides are checked and, if possible, automatically corrected.
Quickstart Guide
We strongly recommend you familiarize yourself with some of the ALR Simulationframework's core concepts.
Please refer the Guide Chapters marked as [recommended].
Helpful Resources
Physic engines:
Gyms & Reinforcement Learning:
About
A stable and public version of SimulationFramework for KIT Robot Praktikum usage
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
4 watching
Forks
10 forks
Report repository
Releases
Packages
Contributors
Languages