readme

README.md

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-<div align="center">
-<img src="https://github.com/Lifelong-Robot-Learning/LIBERO/blob/master/images/libero_logo.png" width="360">
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-<p align="center">
-<a href="https://github.com/Lifelong-Robot-Learning/LIBERO/actions">
-<img alt="Tests Passing" src="https://github.com/anuraghazra/github-readme-stats/workflows/Test/badge.svg" />
-</a>
-<a href="https://github.com/Lifelong-Robot-Learning/LIBERO/graphs/contributors">
-<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/Lifelong-Robot-Learning/LIBERO" />
-</a>
-<a href="https://github.com/Lifelong-Robot-Learning/LIBERO/issues">
-<img alt="Issues" src="https://img.shields.io/github/issues/Lifelong-Robot-Learning/LIBERO?color=0088ff" />
-
-## **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/)
-______________________________________________________________________
-![pull_figure](https://github.com/Lifelong-Robot-Learning/LIBERO/blob/master/images//fig1.png)
-</div>
-
-**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.
-
----
-
-
-# Contents
-
-- [Installation](#Installation)
-- [Datasets](#Dataset)
-- [Getting Started](#Getting-Started)
-  - [Task](#Task)
-  - [Training](#Training)
-  - [Evaluation](#Evaluation)
-- [Citation](#Citation)
-- [License](#License)
-
-
-# Installtion
-Please run the following commands in the given order to install the dependency for **LIBERO**.
-```
-conda create -n libero python=3.8.13
-conda activate libero
-git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git
-cd LIBERO
-pip install -r requirements.txt
-pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
-```
-
-Then install the `libero` package:
-```
-pip install -e .
-```
-
-# Datasets
-We provide high-quality human teleoperation demonstrations for the four task suites in **LIBERO**. To download the demonstration dataset, run:
-```python
-python benchmark_scripts/download_libero_datasets.py
-```
-By default, the dataset will be stored under the ```LIBERO``` folder and all four datasets will be downloaded. To download a specific dataset, use
-```python
-python benchmark_scripts/download_libero_datasets.py --datasets DATASET
-```
-where ```DATASET``` is chosen from `[libero_spatial, libero_object, libero_100, libero_goal`.
-
-
-# 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.
-```python
-from libero.libero import benchmark
-from libero.libero.envs import OffScreenRenderEnv
-
-
-benchmark_dict = benchmark.get_benchmark_dict()
-task_suite_name = "libero_10" # can also choose libero_spatial, libero_object, etc.
-task_suite = benchmark_dict[task_suite_name]()
-
-# retrieve a specific task
-task_id = 0
-task = task_suite.get_task(task_id)
-task_name = task.name
-task_description = task.language
-task_bddl_file = os.path.join(get_libero_path("bddl_files"), task.problem_folder, task.bddl_file)
-print(f"[info] retrieving task {task_id} from suite {task_suite_name}, the " + \
-      f"language instruction is {task_description}, and the bddl file is {task_bddl_file}")
-
-# step over the environment
-env_args = {
-    "bddl_file_name": task_bddl_file,
-    "camera_heights": 128,
-    "camera_widths": 128
-}
-env = OffScreenRenderEnv(**env_args)
-env.seed(0)
-env.reset()
-init_states = task_suite.get_task_init_states(task_id) # for benchmarking purpose, we fix the a set of initial states
-init_state_id = 0
-env.set_init_state(init_states[init_state_id])
-
-dummy_action = [0.] * 7
-for step in range(10):
-    obs, reward, done, info = env.step(dummy_action)
-env.close()
-```
-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, please choose:
-- `BENCHMARK` from `[LIBERO_SPATIAL, LIBERO_OBJECT, LIBERO_GOAL, LIBERO_90, LIBERO_10]`
-- `POLICY` from `[bc_rnn_policy, bc_transformer_policy, bc_vilt_policy]`
-- `ALGO` from `[base, er, ewc, packnet, multitask]`
-
-then run the following:
-
-```shell
-export CUDA_VISIBLE_DEVICES=GPU_ID && \
-export MUJOCO_EGL_DEVICE_ID=GPU_ID && \
-python libero/lifelong/main.py seed=SEED \
-                               benchmark_name=BENCHMARK \
-                               policy=POLICY \
-                               lifelong=ALGO
-```
-Please see the documentation for the details of reproducing the study results.
-
-## Evaluation
-
-By default the policies will be evaluated on the fly during training. If you have limited computing resource of GPUs, we offer an evaluation script for you to evaluate models separately.
-
-```shell
-python libero/lifelong/evaluate.py --benchmark BENCHMARK_NAME \
-                                   --task_id TASK_ID \ 
-                                   --algo ALGO_NAME \
-                                   --policy POLICY_NAME \
-                                   --seed SEED \
-                                   --ep EPOCH \
-                                   --load_task LOAD_TASK \
-                                   --device_id CUDA_ID
-```
-
-# Citation
-If you find **LIBERO** to be useful in your own research, please consider citing our paper:
-
-```bibtex
-@article{liu2023libero,
-  title={LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
-  author={Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and Liu, Qiang and Zhu, Yuke and Stone, Peter},
-  journal={arXiv preprint arXiv:2306.03310},
-  year={2023}
-}
-```
-
-# License
-| Component        | License                                                                                                                             |
-|------------------|-------------------------------------------------------------------------------------------------------------------------------------|
-| Codebase         | [MIT License](LICENSE)                                                                                                                      |
-| Datasets         | [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode)                 |
+Benchmark for Generalization on top of **LIBERO** (https://libero-project.github.io)