Lifelong-Robot-Learning / LIBERO

## **Benchmarking Knowledge Transfer for Lifelong Robot Learning**
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone
[[Website]](https://libero-project.github.io)
[[Paper]](https://arxiv.org/pdf/2306.03310.pdf)
[[Docs]](https://lifelong-robot-learning.github.io/LIBERO/)
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**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.
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# Contents
- Installation
- Datasets
- Getting Started
 - Task
 - Training
 - Evaluation
- Citation
- License
# Installtion
Please run the following commands in the given order to install the dependency for **LIBERO**.
 [code] 
Then install the `libero` package:
 [code] 
# Datasets
We provide high-quality human teleoperation demonstrations for the four task suites in **LIBERO**. To download the demonstration dataset, run:
 [code] 
By default, the dataset will be stored under the [code] folder and all four datasets will be downloaded. To download a specific dataset, use
 [code] 
where [code] is chosen from `libero_spatial, libero_object, libero_100, libero_goal`.
**NEW!!!**
Alternatively, you can download the dataset from HuggingFace by using:
 [code] 
This option can also be combined with the specific dataset selection:
 [code] 
The datasets hosted on HuggingFace are available at [here.
# Getting Started
For a detailed walk-through, please either refer to the documentation or the notebook examples provided under the `notebooks` folder. In the following, we provide example scripts for retrieving a task, training and evaluation.
## Task
The following is a minimal example of retrieving a specific task from a specific task suite.
 [code] 
Currently, we only support sparse reward function (i.e., the agent receives `+1` when the task is finished). As sparse-reward RL is extremely hard to learn, currently we mainly focus on lifelong imitation learning.
## Training
To start a lifelong learning experiment,