Datasets:

zjunlp
/

OceanGym

@@ -1,631 +1,638 @@
-<h1 align="center"> 🌊 OceanGym 🦾 </h1>
-<h3 align="center"> A Benchmark Environment for Underwater Embodied Agents </h3>
-<p align="center">
-  🌐 <a href="https://oceangpt.github.io/OceanGym" target="_blank">Home Page</a>
-  📄 <a href="https://arxiv.org/abs/123" target="_blank">ArXiv Paper</a>
-  🤗 <a href="https://huggingface.co/datasets/zjunlp/OceanGym" target="_blank">Hugging Face</a>
-  ☁️ <a href="https://drive.google.com/drive/folders/1H7FTbtOCKTIEGp3R5RNsWvmxZ1oZxQih?usp=sharing" target="_blank">Google Drive</a>
-</p>
-  <img src="asset\img\o1.png" align=center>
-**OceanGym** is a high-fidelity embodied underwater environment that simulates a realistic ocean setting with diverse scenes. As illustrated in figure, OceanGym establishes a robust benchmark for evaluating autonomous agents through a series of challenging tasks, encompassing various perception analyses and decision-making navigation. The platform facilitates these evaluations by supporting multi-modal perception and providing action spaces for continuous control.
-# 💐 Acknowledgement
-OceanGym environment is based on Unreal Engine (UE) 5.3.
-Partial functions of OceanGym is developed on [HoloOcean](https://github.com/byu-holoocean).
-Thanks for their great contributions!
-# 🔔 News
-- 09-2025, we launched the OceanGym project.
-- 08-2025, we finshed the OceanGym environment.
----
-**Contents:**
-- [💐 Acknowledgement](#-acknowledgement)
-- [🔔 News](#-news)
-- [📺 Quick Start](#-quick-start)
-  - [Decision Task](#decision-task)
-  - [Perception Task](#perception-task)
-- [⚙️ Set up Environment](#️-set-up-environment)
-  - [Clone HoloOcean](#clone-holoocean)
-  - [Packaged Installation](#packaged-installation)
-  - [Add World Files](#add-world-files)
-  - [Open the World](#open-the-world)
-- [🧠 Decision Task](#-decision-task)
-  - [Target Object Locations](#target-object-locations)
-  - [Evaluation Criteria](#evaluation-criteria)
-- [👀 Perception Task](#-perception-task)
-  - [Using the Bench to Eval](#using-the-bench-to-eval)
-    - [Import Data](#import-data)
-    - [Set your Model Parameters](#set-your-model-parameters)
-    - [Simple Multi-views](#simple-multi-views)
-    - [Multi-views with Sonar](#multi-views-with-sonar)
-    - [Multi-views add Sonar Examples](#multi-views-add-sonar-examples)
-  - [Collecting Image Data](#collecting-image-data)
-    - [Modify Configuration File](#modify-configuration-file)
-    - [Collect Camera Images Only](#collect-camera-images-only)
-    - [Collect Camera and Sonar Images](#collect-camera-and-sonar-images)
-- [⏱️ Results](#️-results)
-  - [Decision Task](#decision-task-1)
-  - [Perception Task](#perception-task-1)
-- [🚩 Citation](#-citation)
-# 📺 Quick Start
-Install the experimental code environment using pip:
-```bash
-pip install -r requirements.txt
-```
-## Decision Task
-> Only the environment is ready! Build the environment based on [here](#️-set-up-environment).
-**Step 1: Run a Task Script**
-   For example, to run task 4:
-   ```bash
-   python decision\tasks\task4.py
-   ```
-   Follow the keyboard instructions or switch to LLM mode for automatic decision-making.
-**Step 2: Keyboard Control Guide**
-   | Key         | Action                        |
-   |-------------|------------------------------|
-   | W           | Move Forward                 |
-   | S           | Move Backward                |
-   | A           | Move Left                    |
-   | D           | Move Right                   |
-   | J           | Turn Left                    |
-   | L           | Turn Right                   |
-   | I           | Move Up                      |
-   | K           | Move Down                    |
-   | M           | Switch to LLM Mode           |
-   | Q           | Exit                         |
-   > You can use WASD for movement, J/L for turning, I/K for up/down.
-   > Press `M` to switch to large language model mode (may cause temporary lag).
-   > Press `Q` to exit.
-**Step 3: View Results**
-   Logs and memory files are automatically saved in the `log/` and `memory/` directories.
-**Step 4: Evaluate the results**
-   Place the generated `memory` and `important_memory` files into the corresponding `point` folders.
-   Then, set the evaluation paths in the `evaluate.py` file.
-   We provide 6 experimental evaluation paths. In `evaluate.py`, you can configure them as follows:
-   ```python
-   eval_roots = [
-       os.path.join(eval_root, "main", "gpt4omini"),
-       os.path.join(eval_root, "main", "gemini"),
-       os.path.join(eval_root, "main", "qwen"),
-       os.path.join(eval_root, "migration", "gpt4o"),
-       os.path.join(eval_root, "migration", "qwen"),
-       os.path.join(eval_root, "scale", "qwen"),
-   ]
-   ```
-   To run the evaluation:
-   ```bash
-   python decision\utils\evaluate.py
-   ```
-   The generated results will be saved under the `\eval\decision` folder.
-## Perception Task
-**Step 1: Prepare the dataset**
-After downloading from [Hugging Face](https://huggingface.co/datasets/zjunlp/OceanGym/tree/main/data/perception), and put it into the `data/perception` folder.
-**Step 2: Select model parameters**
-| parameter | function |
-| ---| --- |
-| model_template | The large language model message queue template you selected. |
-| model_name_or_path | If it is an API model, it is the model name; if it is a local model, it is the path. |
-| api_key | If it is an API model, enter your key. |
-| base_url | If it is an API model, enter its baseful URL. |
-Now we only support OpenAI, Google Gemma, Qwen and OpenBMB.
-```bash
-MODELS_TEMPLATE="Yours"
-MODEL_NAME_OR_PATH="Yours"
-API_KEY="Yours"
-BASE_URL="Yours"
-```
-**Step 3: Run the experiments**
-| parameter | function |
-| ---| --- |
-| exp_name | Customize the name of the experiment to save the results. |
-| exp_idx | Select the experiment number, or enter "all" to select all. |
-| exp_json | JSON file containing the experiment label data. |
-| images_dir | The folder where the experimental image data is stored. |
-For the experimental types, We designed (1) multi-view perception task and (2) context-based perception task.
-For the lighting conditions, We designed (1) high illumination and (2) low illumination.
-For the auxiliary sonar, We designed (1) without sonar image (2) zero-shot sonar image and (3) sonar image with few sonar example.
-Such as this command is used to evaluate the **multi-view** perception task under **high** illumination:
-```bash
-python perception/eval/mv.py \
-    --exp_name Result_MV_highLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLight.json" \
-    --images_dir "/data/perception/highLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-For more patterns about perception tasks, please read [this](#-perception-task) part carefully.
-# ⚙️ Set up Environment
-This project is based on the HoloOcean environment. 💐
-> We have placed a simplified version here. If you encounter any detailed issues, please refer to the [original installation document](https://byu-holoocean.github.io/holoocean-docs/v2.1.0/usage/installation.html).
-## Clone HoloOcean
-Make sure your GitHub account is linked to an **Epic Games** account, please Follow the steps [here](https://www.unrealengine.com/en-US/ue-on-github) and remember to accept the email invitation from Epic Games.
-After that clone HoloOcean:
-```bash
-git clone git@github.com:byu-holoocean/HoloOcean.git holoocean
-```
-## Packaged Installation
-1. Additional Requirements
-For the build-essential package for Linux, you can run the following console command:
-```bash
-sudo apt install build-essential
-```
-2. Python Library
-From the cloned repository, install the Python package by doing the following:
-```bash
-cd holoocean/client
-pip install .
-```
-3. Worlds Packages
-To install the most recent version of the Ocean worlds package, open a Python shell by typing the following and hit enter:
-```bash
-python
-```
-Install the package by running the following Python commands:
-```python
-import holoocean
-holoocean.install("Ocean")
-```
-To do these steps in a single console command, use:
-```bash
-python -c "import holoocean; holoocean.install('Ocean')"
-```
-## Add World Files
-Place the JSON config file from `asset/decision/map_config` or `asset\perception\map_config` into some place like:
-(Windows)
-```
-C:\Users\Windows\AppData\Local\holoocean\2.0.0\worlds\Ocean
-```
-## Open the World
-**1. If you're use it in first time, you have to compile it**
-  1-1. find the Holodeck.uproject in **engine** folder \
-  <img src="asset\img\pic1.png" style="width: 60%; height: auto;" align="center">
-  1-2. Right-click and select:Generate Visual Studio project files \
-    <img src="asset\img\pic2.png" style="width: 60%; height: auto;" align="center">
-  1-3. If the version is not 5.3.2,please choose the Switch Unreal Engine Version \
-    <img src="asset\img\pic3.png" style="width: 60%; height: auto;" align="center">
-  1-4. Then open the project \
-    <img src="asset\img\pic4.png" style="width: 60%; height: auto;" align="center">
-**2. Then find the `HAIDI` map in `demo` directory** \
-    <img src="asset\img\pic5.png" style="width: 60%; height: auto;" align="center">
-**3. Run the project** \
-    <img src="asset\img\pic6.png" style="width: 60%; height: auto;" align="center">
-# 🧠 Decision Task
-> All commands are applicable to **Windows** only, because it requires full support from the `UE5 Engine`.
-The decision experiment can be run with reference to the [Quick Start](#️-quick-start).
-## Target Object Locations
-We have provided eight tasks. For specific task descriptions, please refer to the paper.
-The following are the coordinates for each target object in the environment (in meters):
-- **MINING ROBOT**:
-  (-71, 149, -61), (325, -47, -83)
-- **OIL PIPELINE**:
-  (345, -165, -32), (539, -233, -42), (207, -30, -66)
-- **OIL DRUM**:
-  (447, -203, -98)
-- **SUNKEN SHIP**:
-  (429, -151, -69), (78, -11, -47)
-- **ELECTRICAL BOX**:
-  (168, 168, -65)
-- **WIND POWER STATION**:
-  (207, -30, -66)
-- **AIRCRAFT WRECKAGE**:
-  (40, -9, -54), (296, 78, -70), (292, -186, -67)
-- **H-MARKED LANDING PLATFORM**:
-  (267, 33, -80)
----
-## Evaluation Criteria
-1. If the target is not found, use the final stopping position for evaluation.
-2. If the target is found, use the closest distance to any target point.
-3. For found targets:
-   - Minimum distance ≤ 30: full score
-   - 30 < distance < 100: score decreases proportionally
-   - Distance ≥ 100: score is 0
-4. Score composition:
-   - One point: 100
-   - Two points: 60 / 40
-   - Three points: 60 / 20 / 20
-# 👀 Perception Task
-## Using the Bench to Eval
-> All commands are applicable to **Linux**, so if you using **Windows**, you need to change the corresponding path representation (especially the slash).
->
-> Now we only support OpenAI, Google Gemma, Qwen and OpenBMB. If you need to customize the model, please contact the author.
-### Import Data
-First, you need download our data from [Hugging Face](https://huggingface.co/datasets/zjunlp/OceanGym).
-And then create a new `data` folder in the project root directory:
-```bash
-mkdir -p data/perception
-```
-Finally, put the downloaded data into the corresponding folder.
-### Set your Model Parameters
-Just open a terminal in the root directory and set it directly.
-| parameter | function |
-| ---| --- |
-| model_template | The large language model message queue template you selected. |
-| model_name_or_path | If it is an API model, it is the model name; if it is a local model, it is the path. |
-| api_key | If it is an API model, enter your key. |
-| base_url | If it is an API model, enter its baseful URL. |
-```bash
-MODELS_TEMPLATE="Yours"
-MODEL_NAME_OR_PATH="Yours"
-API_KEY="Yours"
-BASE_URL="Yours"
-```
-### Simple Multi-views
-All of these scripts evaluate the perception task, and the parameters are as follows:
-| parameter | function |
-| ---| --- |
-| exp_name | Customize the name of the experiment to save the results. |
-| exp_idx | Select the experiment number, or enter "all" to select all. |
-| exp_json | JSON file containing the experiment label data. |
-| images_dir | The folder where the experimental image data is stored. |
-This command is used to evaluate the **multi-view** perception task under **high** illumination:
-```bash
-python perception/eval/mv.py \
-    --exp_name Result_MV_highLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLight.json" \
-    --images_dir "/data/perception/highLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **context-based** perception task under **high** illumination:
-```bash
-python perception/eval/mv.py \
-    --exp_name Result_MV_highLightContext_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLightContext.json" \
-    --images_dir "/data/perception/highLightContext" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **multi-view** perception task under **low** illumination:
-```bash
-python perception/eval/mv.py \
-    --exp_name Result_MV_lowLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/lowLight.json" \
-    --images_dir "/data/perception/lowLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **context-based** perception task under **low** illumination:
-```bash
-python perception/eval/mv.py \
-    --exp_name Result_MV_lowLightContext_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/lowLightContext.json" \
-    --images_dir "/data/perception/lowLightContext" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-### Multi-views with Sonar
-This command is used to evaluate the **multi-view** perception task under **high** illumination with **sonar** image:
-```bash
-python perception/eval/mvs.py \
-    --exp_name Result_MVwS_highLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLight.json" \
-    --images_dir "/data/perception/highLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **context-based** perception task under **high** illumination with **sonar** image:
-```bash
-python perception/eval/mvs.py \
-    --exp_name Result_MVwS_highLightContext_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLightContext.json" \
-    --images_dir "/data/perception/highLightContext" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **multi-view** perception task under **low** illumination with **sonar** image:
-```bash
-python perception/eval/mvs.py \
-    --exp_name Result_MVwS_lowLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/lowLight.json" \
-    --images_dir "/data/perception/lowLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **context-based** perception task under **low** illumination with **sonar** image:
-```bash
-python perception/eval/mvs.py \
-    --exp_name Result_MVwS_lowLightContext_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/lowLightContext.json" \
-    --images_dir "/data/perception/lowLightContext" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-### Multi-views add Sonar Examples
-This command is used to evaluate the **multi-view** perception task under **high** illumination with **sona** image **examples**:
-```bash
-python perception/eval/mvsex.py \
-    --exp_name Result_MVwSss_highLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLight.json" \
-    --images_dir "/data/perception/highLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **context-based** perception task under **high** illumination with **sona** image **examples**:
-```bash
-python perception/eval/mvsex.py \
-    --exp_name Result_MVwSss_highLightContext_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/highLightContext.json" \
-    --images_dir "/data/perception/highLightContext" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **multi-view** perception task under **low** illumination with **sona** image **examples**:
-```bash
-python perception/eval/mvsex.py \
-    --exp_name Result_MVwSss_lowLight_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/lowLight.json" \
-    --images_dir "/data/perception/lowLight" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-This command is used to evaluate the **context-based** perception task under **low** illumination with **sona** image **examples**:
-```bash
-python perception/eval/mvsex.py \
-    --exp_name Result_MVwSss_lowLightContext_00 \
-    --exp_idx "all" \
-    --exp_json "/data/perception/lowLightContext.json" \
-    --images_dir "/data/perception/lowLightContext" \
-    --model_template $MODELS_TEMPLATE \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --api_key $API_KEY \
-    --base_url $BASE_URL
-```
-## Collecting Image Data
-> This part is optional. Only use when you need to collect pictures by yourself.
-### Modify Configuration File
-The sample configuration files can be found in `asset/perception/map_config`. You need to copy this and paste it into your HoloOcean project's configuration.
-### Collect Camera Images Only
-This command is used to collect **camera** images only, and the parameters are as follows:
-| parameter | function |
-| ---| --- |
-| scenario | The name of the json configuration file you want to replace. |
-| task_name | Customize the name of the experiment to save the results. |
-| rgbcamera | The camera directions you can choose. If select all, enter "all". |
-```bash
-python perception/task/init_map.py \
-    --scenario without_sonar \
-    --task_name "Exp_Camera_Only" \
-    --rgbcamera "all"
-```
-### Collect Camera and Sonar Images
-This command is used to collect both **camera** images and **sonar** images at same time:
-```bash
-python perception/task/init_map_with_sonar.py \
-    --scenario with_sonar \
-    --task_name "Exp_Add_Sonar" \
-    --rgbcamera "FrontCamera"
-```
-# ⏱️ Results
-## Decision Task
-  <img src="asset\img\t1.png" align=center>
-- This table is the performance in decision tasks requiring autonomous completion by MLLM-driven agents.
-## Perception Task
-  <img src="asset\img\t2.png" align=center>
-- This table is the performance of perception tasks across different models and conditions.
-- Values represent accuracy percentages.
-- Adding sonar means using both RGB and sonar images.
-# 🚩 Citation
-If this OceanGym paper or benchmark is helpful, please kindly cite as this:
-```bibtex
-@inproceedings{xxx,
-  title={OceanGym: A Benchmark Environment for Underwater Embodied Agents},
-  ...
-}
-```
-General HoloOcean use:
-```bibtex
-@inproceedings{Potokar22icra,
-   author = {E. Potokar and S. Ashford and M. Kaess and J. Mangelson},
-   title = {Holo{O}cean: An Underwater Robotics Simulator},
-   booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
-   address = {Philadelphia, PA, USA},
-   month = may,
-   year = {2022}
-}
-```
-Simulation of Sonar (Imaging, Profiling, Sidescan) sensors:
-```bibtex
-@inproceedings{Potokar22iros,
-   author = {E. Potokar and K. Lay and K. Norman and D. Benham and T. Neilsen and M. Kaess and J. Mangelson},
-   title = {Holo{O}cean: Realistic Sonar Simulation},
-   booktitle = {Proc. IEEE/RSJ Intl. Conf. Intelligent Robots and Systems, IROS},
-   address = {Kyoto, Japan},
-   month = {Oct},
-   year = {2022}
-}
-```
-💐 Thanks again!

+---
+license: mit
+language:
+- en
+tags:
+- agent
+---
+<h1 align="center"> 🌊 OceanGym 🦾 </h1>
+<h3 align="center"> A Benchmark Environment for Underwater Embodied Agents </h3>
+<p align="center">
+  🌐 <a href="https://oceangpt.github.io/OceanGym" target="_blank">Home Page</a>
+  📄 <a href="https://arxiv.org/abs/123" target="_blank">ArXiv Paper</a>
+  🤗 <a href="https://huggingface.co/datasets/zjunlp/OceanGym" target="_blank">Hugging Face</a>
+  ☁️ <a href="https://drive.google.com/drive/folders/1H7FTbtOCKTIEGp3R5RNsWvmxZ1oZxQih?usp=sharing" target="_blank">Google Drive</a>
+</p>
+  <img src="asset\img\o1.png" align=center>
+**OceanGym** is a high-fidelity embodied underwater environment that simulates a realistic ocean setting with diverse scenes. As illustrated in figure, OceanGym establishes a robust benchmark for evaluating autonomous agents through a series of challenging tasks, encompassing various perception analyses and decision-making navigation. The platform facilitates these evaluations by supporting multi-modal perception and providing action spaces for continuous control.
+# 💐 Acknowledgement
+OceanGym environment is based on Unreal Engine (UE) 5.3.
+Partial functions of OceanGym is developed on [HoloOcean](https://github.com/byu-holoocean).
+Thanks for their great contributions!
+# 🔔 News
+- 09-2025, we launched the OceanGym project.
+- 08-2025, we finshed the OceanGym environment.
+---
+**Contents:**
+- [💐 Acknowledgement](#-acknowledgement)
+- [🔔 News](#-news)
+- [📺 Quick Start](#-quick-start)
+  - [Decision Task](#decision-task)
+  - [Perception Task](#perception-task)
+- [⚙️ Set up Environment](#️-set-up-environment)
+  - [Clone HoloOcean](#clone-holoocean)
+  - [Packaged Installation](#packaged-installation)
+  - [Add World Files](#add-world-files)
+  - [Open the World](#open-the-world)
+- [🧠 Decision Task](#-decision-task)
+  - [Target Object Locations](#target-object-locations)
+  - [Evaluation Criteria](#evaluation-criteria)
+- [👀 Perception Task](#-perception-task)
+  - [Using the Bench to Eval](#using-the-bench-to-eval)
+    - [Import Data](#import-data)
+    - [Set your Model Parameters](#set-your-model-parameters)
+    - [Simple Multi-views](#simple-multi-views)
+    - [Multi-views with Sonar](#multi-views-with-sonar)
+    - [Multi-views add Sonar Examples](#multi-views-add-sonar-examples)
+  - [Collecting Image Data](#collecting-image-data)
+    - [Modify Configuration File](#modify-configuration-file)
+    - [Collect Camera Images Only](#collect-camera-images-only)
+    - [Collect Camera and Sonar Images](#collect-camera-and-sonar-images)
+- [⏱️ Results](#️-results)
+  - [Decision Task](#decision-task-1)
+  - [Perception Task](#perception-task-1)
+- [🚩 Citation](#-citation)
+# 📺 Quick Start
+Install the experimental code environment using pip:
+```bash
+pip install -r requirements.txt
+```
+## Decision Task
+> Only the environment is ready! Build the environment based on [here](#️-set-up-environment).
+**Step 1: Run a Task Script**
+   For example, to run task 4:
+   ```bash
+   python decision\tasks\task4.py
+   ```
+   Follow the keyboard instructions or switch to LLM mode for automatic decision-making.
+**Step 2: Keyboard Control Guide**
+   | Key         | Action                        |
+   |-------------|------------------------------|
+   | W           | Move Forward                 |
+   | S           | Move Backward                |
+   | A           | Move Left                    |
+   | D           | Move Right                   |
+   | J           | Turn Left                    |
+   | L           | Turn Right                   |
+   | I           | Move Up                      |
+   | K           | Move Down                    |
+   | M           | Switch to LLM Mode           |
+   | Q           | Exit                         |
+   > You can use WASD for movement, J/L for turning, I/K for up/down.
+   > Press `M` to switch to large language model mode (may cause temporary lag).
+   > Press `Q` to exit.
+**Step 3: View Results**
+   Logs and memory files are automatically saved in the `log/` and `memory/` directories.
+**Step 4: Evaluate the results**
+   Place the generated `memory` and `important_memory` files into the corresponding `point` folders.
+   Then, set the evaluation paths in the `evaluate.py` file.
+   We provide 6 experimental evaluation paths. In `evaluate.py`, you can configure them as follows:
+   ```python
+   eval_roots = [
+       os.path.join(eval_root, "main", "gpt4omini"),
+       os.path.join(eval_root, "main", "gemini"),
+       os.path.join(eval_root, "main", "qwen"),
+       os.path.join(eval_root, "migration", "gpt4o"),
+       os.path.join(eval_root, "migration", "qwen"),
+       os.path.join(eval_root, "scale", "qwen"),
+   ]
+   ```
+   To run the evaluation:
+   ```bash
+   python decision\utils\evaluate.py
+   ```
+   The generated results will be saved under the `\eval\decision` folder.
+## Perception Task
+**Step 1: Prepare the dataset**
+After downloading from [Hugging Face](https://huggingface.co/datasets/zjunlp/OceanGym/tree/main/data/perception), and put it into the `data/perception` folder.
+**Step 2: Select model parameters**
+| parameter | function |
+| ---| --- |
+| model_template | The large language model message queue template you selected. |
+| model_name_or_path | If it is an API model, it is the model name; if it is a local model, it is the path. |
+| api_key | If it is an API model, enter your key. |
+| base_url | If it is an API model, enter its baseful URL. |
+Now we only support OpenAI, Google Gemma, Qwen and OpenBMB.
+```bash
+MODELS_TEMPLATE="Yours"
+MODEL_NAME_OR_PATH="Yours"
+API_KEY="Yours"
+BASE_URL="Yours"
+```
+**Step 3: Run the experiments**
+| parameter | function |
+| ---| --- |
+| exp_name | Customize the name of the experiment to save the results. |
+| exp_idx | Select the experiment number, or enter "all" to select all. |
+| exp_json | JSON file containing the experiment label data. |
+| images_dir | The folder where the experimental image data is stored. |
+For the experimental types, We designed (1) multi-view perception task and (2) context-based perception task.
+For the lighting conditions, We designed (1) high illumination and (2) low illumination.
+For the auxiliary sonar, We designed (1) without sonar image (2) zero-shot sonar image and (3) sonar image with few sonar example.
+Such as this command is used to evaluate the **multi-view** perception task under **high** illumination:
+```bash
+python perception/eval/mv.py \
+    --exp_name Result_MV_highLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLight.json" \
+    --images_dir "/data/perception/highLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+For more patterns about perception tasks, please read [this](#-perception-task) part carefully.
+# ⚙️ Set up Environment
+This project is based on the HoloOcean environment. 💐
+> We have placed a simplified version here. If you encounter any detailed issues, please refer to the [original installation document](https://byu-holoocean.github.io/holoocean-docs/v2.1.0/usage/installation.html).
+## Clone HoloOcean
+Make sure your GitHub account is linked to an **Epic Games** account, please Follow the steps [here](https://www.unrealengine.com/en-US/ue-on-github) and remember to accept the email invitation from Epic Games.
+After that clone HoloOcean:
+```bash
+git clone git@github.com:byu-holoocean/HoloOcean.git holoocean
+```
+## Packaged Installation
+1. Additional Requirements
+For the build-essential package for Linux, you can run the following console command:
+```bash
+sudo apt install build-essential
+```
+2. Python Library
+From the cloned repository, install the Python package by doing the following:
+```bash
+cd holoocean/client
+pip install .
+```
+3. Worlds Packages
+To install the most recent version of the Ocean worlds package, open a Python shell by typing the following and hit enter:
+```bash
+python
+```
+Install the package by running the following Python commands:
+```python
+import holoocean
+holoocean.install("Ocean")
+```
+To do these steps in a single console command, use:
+```bash
+python -c "import holoocean; holoocean.install('Ocean')"
+```
+## Add World Files
+Place the JSON config file from `asset/decision/map_config` or `asset\perception\map_config` into some place like:
+(Windows)
+```
+C:\Users\Windows\AppData\Local\holoocean\2.0.0\worlds\Ocean
+```
+## Open the World
+**1. If you're use it in first time, you have to compile it**
+  1-1. find the Holodeck.uproject in **engine** folder \
+  <img src="asset\img\pic1.png" style="width: 60%; height: auto;" align="center">
+  1-2. Right-click and select:Generate Visual Studio project files \
+    <img src="asset\img\pic2.png" style="width: 60%; height: auto;" align="center">
+  1-3. If the version is not 5.3.2,please choose the Switch Unreal Engine Version \
+    <img src="asset\img\pic3.png" style="width: 60%; height: auto;" align="center">
+  1-4. Then open the project \
+    <img src="asset\img\pic4.png" style="width: 60%; height: auto;" align="center">
+**2. Then find the `HAIDI` map in `demo` directory** \
+    <img src="asset\img\pic5.png" style="width: 60%; height: auto;" align="center">
+**3. Run the project** \
+    <img src="asset\img\pic6.png" style="width: 60%; height: auto;" align="center">
+# 🧠 Decision Task
+> All commands are applicable to **Windows** only, because it requires full support from the `UE5 Engine`.
+The decision experiment can be run with reference to the [Quick Start](#️-quick-start).
+## Target Object Locations
+We have provided eight tasks. For specific task descriptions, please refer to the paper.
+The following are the coordinates for each target object in the environment (in meters):
+- **MINING ROBOT**:
+  (-71, 149, -61), (325, -47, -83)
+- **OIL PIPELINE**:
+  (345, -165, -32), (539, -233, -42), (207, -30, -66)
+- **OIL DRUM**:
+  (447, -203, -98)
+- **SUNKEN SHIP**:
+  (429, -151, -69), (78, -11, -47)
+- **ELECTRICAL BOX**:
+  (168, 168, -65)
+- **WIND POWER STATION**:
+  (207, -30, -66)
+- **AIRCRAFT WRECKAGE**:
+  (40, -9, -54), (296, 78, -70), (292, -186, -67)
+- **H-MARKED LANDING PLATFORM**:
+  (267, 33, -80)
+---
+## Evaluation Criteria
+1. If the target is not found, use the final stopping position for evaluation.
+2. If the target is found, use the closest distance to any target point.
+3. For found targets:
+   - Minimum distance ≤ 30: full score
+   - 30 < distance < 100: score decreases proportionally
+   - Distance ≥ 100: score is 0
+4. Score composition:
+   - One point: 100
+   - Two points: 60 / 40
+   - Three points: 60 / 20 / 20
+# 👀 Perception Task
+## Using the Bench to Eval
+> All commands are applicable to **Linux**, so if you using **Windows**, you need to change the corresponding path representation (especially the slash).
+>
+> Now we only support OpenAI, Google Gemma, Qwen and OpenBMB. If you need to customize the model, please contact the author.
+### Import Data
+First, you need download our data from [Hugging Face](https://huggingface.co/datasets/zjunlp/OceanGym).
+And then create a new `data` folder in the project root directory:
+```bash
+mkdir -p data/perception
+```
+Finally, put the downloaded data into the corresponding folder.
+### Set your Model Parameters
+Just open a terminal in the root directory and set it directly.
+| parameter | function |
+| ---| --- |
+| model_template | The large language model message queue template you selected. |
+| model_name_or_path | If it is an API model, it is the model name; if it is a local model, it is the path. |
+| api_key | If it is an API model, enter your key. |
+| base_url | If it is an API model, enter its baseful URL. |
+```bash
+MODELS_TEMPLATE="Yours"
+MODEL_NAME_OR_PATH="Yours"
+API_KEY="Yours"
+BASE_URL="Yours"
+```
+### Simple Multi-views
+All of these scripts evaluate the perception task, and the parameters are as follows:
+| parameter | function |
+| ---| --- |
+| exp_name | Customize the name of the experiment to save the results. |
+| exp_idx | Select the experiment number, or enter "all" to select all. |
+| exp_json | JSON file containing the experiment label data. |
+| images_dir | The folder where the experimental image data is stored. |
+This command is used to evaluate the **multi-view** perception task under **high** illumination:
+```bash
+python perception/eval/mv.py \
+    --exp_name Result_MV_highLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLight.json" \
+    --images_dir "/data/perception/highLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **context-based** perception task under **high** illumination:
+```bash
+python perception/eval/mv.py \
+    --exp_name Result_MV_highLightContext_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLightContext.json" \
+    --images_dir "/data/perception/highLightContext" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **multi-view** perception task under **low** illumination:
+```bash
+python perception/eval/mv.py \
+    --exp_name Result_MV_lowLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/lowLight.json" \
+    --images_dir "/data/perception/lowLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **context-based** perception task under **low** illumination:
+```bash
+python perception/eval/mv.py \
+    --exp_name Result_MV_lowLightContext_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/lowLightContext.json" \
+    --images_dir "/data/perception/lowLightContext" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+### Multi-views with Sonar
+This command is used to evaluate the **multi-view** perception task under **high** illumination with **sonar** image:
+```bash
+python perception/eval/mvs.py \
+    --exp_name Result_MVwS_highLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLight.json" \
+    --images_dir "/data/perception/highLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **context-based** perception task under **high** illumination with **sonar** image:
+```bash
+python perception/eval/mvs.py \
+    --exp_name Result_MVwS_highLightContext_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLightContext.json" \
+    --images_dir "/data/perception/highLightContext" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **multi-view** perception task under **low** illumination with **sonar** image:
+```bash
+python perception/eval/mvs.py \
+    --exp_name Result_MVwS_lowLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/lowLight.json" \
+    --images_dir "/data/perception/lowLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **context-based** perception task under **low** illumination with **sonar** image:
+```bash
+python perception/eval/mvs.py \
+    --exp_name Result_MVwS_lowLightContext_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/lowLightContext.json" \
+    --images_dir "/data/perception/lowLightContext" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+### Multi-views add Sonar Examples
+This command is used to evaluate the **multi-view** perception task under **high** illumination with **sona** image **examples**:
+```bash
+python perception/eval/mvsex.py \
+    --exp_name Result_MVwSss_highLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLight.json" \
+    --images_dir "/data/perception/highLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **context-based** perception task under **high** illumination with **sona** image **examples**:
+```bash
+python perception/eval/mvsex.py \
+    --exp_name Result_MVwSss_highLightContext_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/highLightContext.json" \
+    --images_dir "/data/perception/highLightContext" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **multi-view** perception task under **low** illumination with **sona** image **examples**:
+```bash
+python perception/eval/mvsex.py \
+    --exp_name Result_MVwSss_lowLight_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/lowLight.json" \
+    --images_dir "/data/perception/lowLight" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+This command is used to evaluate the **context-based** perception task under **low** illumination with **sona** image **examples**:
+```bash
+python perception/eval/mvsex.py \
+    --exp_name Result_MVwSss_lowLightContext_00 \
+    --exp_idx "all" \
+    --exp_json "/data/perception/lowLightContext.json" \
+    --images_dir "/data/perception/lowLightContext" \
+    --model_template $MODELS_TEMPLATE \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --api_key $API_KEY \
+    --base_url $BASE_URL
+```
+## Collecting Image Data
+> This part is optional. Only use when you need to collect pictures by yourself.
+### Modify Configuration File
+The sample configuration files can be found in `asset/perception/map_config`. You need to copy this and paste it into your HoloOcean project's configuration.
+### Collect Camera Images Only
+This command is used to collect **camera** images only, and the parameters are as follows:
+| parameter | function |
+| ---| --- |
+| scenario | The name of the json configuration file you want to replace. |
+| task_name | Customize the name of the experiment to save the results. |
+| rgbcamera | The camera directions you can choose. If select all, enter "all". |
+```bash
+python perception/task/init_map.py \
+    --scenario without_sonar \
+    --task_name "Exp_Camera_Only" \
+    --rgbcamera "all"
+```
+### Collect Camera and Sonar Images
+This command is used to collect both **camera** images and **sonar** images at same time:
+```bash
+python perception/task/init_map_with_sonar.py \
+    --scenario with_sonar \
+    --task_name "Exp_Add_Sonar" \
+    --rgbcamera "FrontCamera"
+```
+# ⏱️ Results
+## Decision Task
+  <img src="asset\img\t1.png" align=center>
+- This table is the performance in decision tasks requiring autonomous completion by MLLM-driven agents.
+## Perception Task
+  <img src="asset\img\t2.png" align=center>
+- This table is the performance of perception tasks across different models and conditions.
+- Values represent accuracy percentages.
+- Adding sonar means using both RGB and sonar images.
+# 🚩 Citation
+If this OceanGym paper or benchmark is helpful, please kindly cite as this:
+```bibtex
+@inproceedings{xxx,
+  title={OceanGym: A Benchmark Environment for Underwater Embodied Agents},
+  ...
+}
+```
+General HoloOcean use:
+```bibtex
+@inproceedings{Potokar22icra,
+   author = {E. Potokar and S. Ashford and M. Kaess and J. Mangelson},
+   title = {Holo{O}cean: An Underwater Robotics Simulator},
+   booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
+   address = {Philadelphia, PA, USA},
+   month = may,
+   year = {2022}
+}
+```
+Simulation of Sonar (Imaging, Profiling, Sidescan) sensors:
+```bibtex
+@inproceedings{Potokar22iros,
+   author = {E. Potokar and K. Lay and K. Norman and D. Benham and T. Neilsen and M. Kaess and J. Mangelson},
+   title = {Holo{O}cean: Realistic Sonar Simulation},
+   booktitle = {Proc. IEEE/RSJ Intl. Conf. Intelligent Robots and Systems, IROS},
+   address = {Kyoto, Japan},
+   month = {Oct},
+   year = {2022}
+}
+```
+💐 Thanks again!