VLMbench is a robotics manipulation benchmark, which contains various language instructions on categorized robotic manipulation tasks. In this work, we aim to fill the blank of the last mile of embodied agents---object manipulation by following human guidance, e.g., “move the red mug next to the box while keeping it upright.” VLMbench is the first benchmark that compositional designs for vision-and-language reasoning on manipulations and categorizes the manipulation tasks from the perspectives of task constraints. Meanwhile, we introduce an Automatic Manipulation Solver (AMSolver), where modular rule-based task templates are created to automatically generate robot demonstrations with language instructions, consisting of diverse object shapes and appearances, action types, and motion constraints. Click here for website and paper.
This repo include the implementaions of AMSolver, VLMbench, and 6D-CLIPort.
- More starting codes have been added under the examples folder!
- The work has been accepted by NeurIPS 2022 (Datasets and Benchmarks) !
Users can use AMSolver to run the current tasks in the VLMbench or build new tasks. In order to run the AMSolver, you should install Coppliasim 4.1.0 and PyRep first. Then, lets install AMSolver:
pip install -r requirements.txt
pip install -r cliport/requirements.txt #Not needed if don't run 6d-cliport
pip install -e .Then, copy the simAddOnScript_PyRep.lua from current folder into the Coppliasim folder:
cp ./simAddOnScript_PyRep.lua /Path/To/CoppliasimRemember that whenever you re-install the PyRep, the file will be overwritten. Then, you should copy this again.
In order to render observations in headless servers, users need to open the Xorg. First, ensure that the Nvidia driver is appropriately installed. Then, running the following commands:
screen
python ./startx.py 0 #The id of DISPLAYExit the screen session (CTRL+A, D). Any other commands should be run in the different sessions/terminals.
Now, you should find that the X servers are opened on each GPU. To render the application with the first GPU, you should add the following command before running other python codes:
export DISPLAY=:0.0 #Keep the first number as same as the argument of startx; the second number is the id of your gpuThe precollected full dataset can be found at here: Dataset. The smaller sample dataset can be found at here: Sample Dtaset. The dataset is under CC BY 4.0 license.
We also provide a script to automatically download the dataset by using gdrive.
bash ./download_dataset.sh -s /Save/Path/For/Dataset -p Dataset_split -t Tasks
# bash ./download_dataset.sh -h for more help on argumentsThe pretrained models of all baselines can be found at here: Model
To test pretrained 6D-CLIPort models:
python vlm/scripts/cliport_test.py --task TASK_TO_TEST --data_folder /Path/to/VLMbench/Dataset/test --checkpoints_folder /Path/to/Pretained/ModelsTo train new 6D-CLIPort models:
python vlm/scripts/train_baselines.py --data_dir /Path/to/VLMbench/Dataset --train_tasks TASK_NEED_TO_TRAINWe provided several example codes for getting start. Please check the code under examples folder. The gym_test.py shows how to run the vlmbench as gym environment.
Ensure you have gym installed (pip install gymnasium)
To generate new demonstrations for training and validation, users can set the output data directory in save_path parameter and run :
python tools/dataset_generator_NLP.pyMeanwhile, the test configurations can be generated by running:
python tools/test_config_generator.pyAll object models are saved in vlm/object_models. To import new objects into vlmbench, users can use "vlm/object_models/save_model.py". We recommand users first save the object models as a coppeliasim model file (.ttm), then use the extra_from_ttm function inside the save_model.py. More examples can be found in save_model.py.
All tasks templates in the current vlmbench can be found in vlm/tasks. To generate new task templates, users can use "tools/task_builder_NLP".py for basic task template generation. Then, the varations of the task can be written as the child classes of the basic task template. More details can refer the codes of vlm/tasks.
@inproceedings{
zheng2022vlmbench,
title={{VLM}bench: A Compositional Benchmark for Vision-and-Language Manipulation},
author={Kaizhi Zheng and Xiaotong Chen and Odest Jenkins and Xin Eric Wang},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=NAYoSV3tk9}
}