Add docs

docs/calvin.md

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+# CALVIN Benchmark
+
+[CALVIN](https://github.com/mees/calvin) is a benchmark for evaluating vision-language models in robotic long-horizon manipulation tasks. 
+
+![](imgs/calvin.png)
+
+| Method | Mode  | Setting                                      | AVG  | CKPT |
+|--------|-------|----------------------------------------------|------|------|
+| UniVLA   | video sft | ABCD->D       | 4.63 (5x:4.71) | [huggingface](https://huggingface.co/Yuqi1997/UniVLA/tree/main/UNIVLA_CALVIN_ABCD_VIDEO_BS192_8K)  |
+
+## Environment Setup
+We follow the [RoboVLMs](https://github.com/Robot-VLAs/RoboVLMs) repository for environment setup. This setup is only for evaluation. The following steps are required to set up the environment:
+
+```shell
+# Install dependencies
+cd reference/RoboVLMs
+
+# This will install the required environment and download the calvin dataset.
+bash scripts/setup_calvin.sh
+
+# Only for rendering environment.
+bash scripts/setup_calvin_vla.sh
+
+# Check if the environment is set up correctly
+python eval/calvin/env_test.py
+```
+
+## Dataset Preparation
+```shell
+# 1. process the dataset
+python tools/process/calvin_process.py
+
+# 2. extract the vq tokens, need to change the dataset & output path
+bash scripts/tokenizer/extract_vq_emu3.sh 
+
+# 3. pickle generation for training
+python tools/pickle_gen/pickle_generation_calvin.py
+```

docs/libero.md

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+# LIBERO Benchmark
+
+[LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) is a benchmark for studying knowledge transfer in multitask and lifelong robot learning problems. 
+
+![](imgs/libero.png)
+
+## Environment Setup
+```shell
+apt-get install libgl1-mesa-dri
+
+cd reference/RoboVLMs
+
+bash scripts/setup_libero.sh
+```
+
+Dataset download from [huggingface](https://huggingface.co/datasets/openvla/modified_libero_rlds).
+
+## Dataset Preparation
+```shell
+# 1. process the dataset
+python tools/process/libero_process.py
+
+# 2. extract the vq tokens, need to change the dataset & output path
+bash scripts/tokenizer/extract_vq_emu3.sh 
+
+# 3. pickle generation for training
+python tools/pickle_gen/pickle_generation_libero.py\
+  --dataset_path ./datasets/processed_data \
+  --output_path ./datasets/processed_data/meta \
+  --normalizer_path ./configs/normalizer_libero \
+  --output_filename libero_all_norm.pkl
+```