Libero
LIBERO is designed for studying knowledge transfer in multitask and lifelong robot learning problems. Successfully resolving these problems require both declarative knowledge about objects/spatial relationships and procedural knowledge about motion/behaviors. LIBERO provides: a procedural generation pipeline that could in principle generate an infinite number of manipulation tasks. 130 tasks grouped into four task suites: LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-100. The first three task suites have controlled distribution shifts, meaning that they require the transfer of a specific type of knowledge. In contrast, LIBERO-100 consists of 100 manipulation tasks that require the transfer of entangled knowledge. LIBERO-100 is further splitted into LIBERO-90 for pretraining a policy and LIBERO-10 for testing the agent's downstream lifelong learning performance. five research topics. three visuomotor policy network architectures. three lifelong learning algorithms with the sequential finetuning and multitask learning baselines. Please run the following commands in the given order to install the dependency for LIBERO. We provide high-quality human teleoperation demonstrations for the four task suites in LIBERO. To download the demonstration dataset, run:
View Libero on GitHub
LIBERO is designed for studying knowledge transfer in multitask and lifelong robot learning problems. Successfully resolving these problems require both declarative knowledge about objects/spatial relationships and procedural knowledge about motion/behaviors. LIBERO provides: a procedural generation pipeline that could in principle generate an infinite number of manipulation tasks. 130 tasks grouped into four task suites: LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-100. The first three task suites have controlled distribution shifts, meaning that they require the transfer of a specific type of knowledge. In contrast, LIBERO-100 consists of 100 manipulation tasks that require the transfer of entangled knowledge. LIBERO-100 is further splitted into LIBERO-90 for pretraining a policy and LIBERO-10 for testing the agent's downstream lifelong learning performance. five research topics. three visuomotor policy network architectures. three lifelong learning algorithms with the sequential finetuning and multitask learning baselines.
Please run the following commands in the given order to install the dependency for LIBERO.
We provide high-quality human teleoperation demonstrations for the four task suites in LIBERO. To download the demonstration dataset, run:
Libero at a glance
| Stars | 2.4k |
|---|---|
| Forks | 502 |
| Language | Jupyter Notebook |
| License | MIT |
| Last update | 2025-03-15 |
| Contributors | 4 |
How to install Libero
bash git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git
Where Libero is listed
Navid Moazzez
AI business strategist & AI OS builder
Navid Moazzez helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life.
Navid.me is reader-supported. When you buy through links on this site, I may earn an affiliate commission. Learn more.
GitHub repos like this
More repo topics
More free tools
X
Bluesky
LinkedIn
Threads
Mastodon
Instagram
TikTok
Pinterest
YouTube
Amazon KDP
Apple Books
Amazon
Substack
Spotify
Apple Podcasts
Related MCP servers & CLIs
Free AI newsletterThe most actionable AI newsletter for founders
Every week, get proven AI strategies, curated tools, and step-by-step systems to grow your audience, create better content, and build a profitable creator business.
No fluff, no filler, no BS. Just five minutes each week that might level up your online business and life.
P.S. Sign up now to get free access to my ultimate AI tools guide for creators.
