CALVIN
CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks Oier Mees, Lukas Hermann, Erick Rosete, Wolfram Burgard
We present CALVIN (Composing Actions from Language and Vision), an open-source simulated benchmark to learn long-horizon language-conditioned tasks. Our aim is to make it possible to develop agents that can solve many robotic manipulation tasks over a long horizon, from onboard sensors, and specified only via human language. CALVIN tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets and supports flexible specification of sensor suites.
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Quick Start
To begin, clone this repository locally
git clone --recurse-submodules https://github.com/mees/calvin.git
$ export CALVIN_ROOT=$(pwd)/calvin
Install requirements:
$ cd $CALVIN_ROOT
$ virtualenv -p $(which python3) --system-site-packages calvin_env # or use conda
$ source calvin_env/bin/activate
$ sh install.sh
Download dataset (choose which split you want to download with the argument D, ABC or ABCD):
$ cd $CALVIN_ROOT/dataset
$ sh download_data.sh D | ABC | ABCD
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Train Baseline Agent
Train baseline models:
$ cd $CALVIN_ROOT/calvin_models/calvin_agent
$ python training.py
You want to scale your training to a multi-gpu setup? Just specify the number of GPUs and DDP will automatically be used for training thanks to Pytorch Lightning. To train on all available GPUs:
$ python training.py trainer.gpus=-1
If you have access to a Slurm cluster, we also provide trainings scripts here.
You can use Hydra's flexible overriding system for changing hyperparameters. For example, to train a model with rgb images from both static camera and the gripper camera:
$ python training.py datamodule/observation_space=lang_rgb_static_gripper model/perceptual_encoder=gripper_cam
To train a model with RGB-D from both cameras:
$ python training.py datamodule/observation_space=lang_rgbd_both model/perceptual_encoder=RGBD_both
To train a model with rgb images from the static camera and visual tactile observations:
$ python training.py datamodule/observation_space=lang_rgb_static_tactile model/perceptual_encoder=static_RGB_tactile
To see all available hyperparameters:
$ python training.py --help
To resume a training, just override the hydra working directory :
$ python training.py hydra.run.dir=runs/my_dir
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Sensory Observations
CALVIN supports a range of sensors commonly utilized for visuomotor control:
- Static camera RGB images - with shape
200x200x3. - Static camera Depth maps - with shape
200x200x1. - Gripper camera RGB images - with shape
200x200x3. - Gripper camera Depth maps - with shape
200x200x1. - Tactile image - with shape
120x160x2x3. - Proprioceptive state - EE position (3), EE orientation in euler angles (3), gripper width (1), joint positions (7), gripper action (1).
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Action Space
In CALVIN, the agent must perform closed-loop continuous control to follow unconstrained language instructions characterizing complex robot manipulation tasks, sending continuous actions to the robot at 30hz. In order to give researchers and practitioners the freedom to experiment with different action spaces, CALVIN supports the following actions spaces:
- Absolute cartesian pose - EE position (3), EE orientation in euler angles (3), gripper action (1).
- Relative cartesian displacement - EE position (3), EE orientation in euler angles (3), gripper action (1).
- Joint action - Joint positions (7), gripper action (1).
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Evaluation: The Calvin Challenge
Long-horizon Multi-task Language Control (LH-MTLC)
The aim of the CALVIN benchmark is to evaluate the learning of long-horizon language-conditioned continuous control policies. In this setting, a single agent must solve complex manipulation tasks by understanding a series of unconstrained language expressions in a row, e.g., “open the drawer. . . pick up the blue block. . . now push the block into the drawer. . . now open the sliding door”. We provide an evaluation protocol with evaluation modes of varying difficulty by choosing different combinations of sensor suites and amounts of training environments. To avoid a biased initial position, the robot is reset to a neutral position before every multi-step sequence.
To evaluate a trained calvin baseline agent, run the following command:
$ cd $CALVIN_ROOT/calvin_models/calvin_agent
$ python evaluation/evaluate_policy.py --dataset_path <PATH/TO/DATASET> --train_folder <PATH/TO/TRAINING/FOLDER>
Optional arguments:
--checkpoint <PATH/TO/CHECKPOINT>: by default, the evaluation loads the last checkpoint in the training log directory. You can instead specify the path to another checkpoint by adding this to the evaluation command.--debug: print debug information and visualize environment.
If you want to evaluate your own model architecture on the CALVIN challenge, you can implement the CustomModel class in evaluate_policy.py as an interface to your agent. You need to implement the following methods:
- __init__(): gets called once at the beginning of the evaluation.
- reset(): gets called at the beginning of each evaluation sequence.
- step(obs, goal): gets called every step and returns the predicted action.
Then evaluate the model by running:
$ python evaluation/evaluate_policy.py --dataset_path <PATH/TO/DATASET> --custom_model
You are also free to use your own language model instead of using the precomputed language embeddings provided by CALVIN. For this, implement CustomLangEmbeddings in evaluate_policy.py and add --custom_lang_embeddings to the evaluation command.
Multi-task Language Control (MTLC)
Alternatively, you can evaluate the policy on single tasks and without resetting the robot to a neutral position. Note that this evaluation is currently only available for our baseline agent.
$ python evaluation/evaluate_policy_singlestep.py --dataset_path <PATH/TO/DATASET> --train_folder <PATH/TO/TRAINING/FOLDER> [--checkpoint <PATH/TO/CHECKPOINT>] [--debug]
Pre-trained Model
Download the MCIL model checkpoint trained on the static camera rgb images on environment D.
$ wget http://calvin.cs.uni-freiburg.de/model_weights/D_D_static_rgb_baseline.zip
$ unzip D_D_static_rgb_baseline.zip
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Relabeling Raw Language Annotations
You want to try learning language conditioned policies in CALVIN with a new awesome language model?
We provide an example script to relabel the annotations with different language model provided in SBert, such as the larger MPNet (paraphrase-mpnet-base-v2) or its corresponding multilingual model (paraphrase-multilingual-mpnet-base-v2). The supported options are "mini", "mpnet" and "multi". If you want to try different SBert models, just change the model name here.
cd $CALVIN_ROOT/calvin_models/calvin_agent
python utils/relabel_with_new_lang_model.py +path=$CALVIN_ROOT/dataset/task_D_D/ +name_folder=new_lang_model_folder model.nlp_model=mpnet
If you additionally want to sample different language annotations for each sequence (from the same task annotations) in the training split run the same command with the parameter reannotate=true.
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SOTA Models
Open-source models that outperform the MCIL baselines from CALVIN:
Contact Oier to add your model here.
Reinforcement Learning with CALVIN
Are you interested in trying reinforcement learning agents for the different manipulation tasks in the CALVIN environment? We provide a google colab to showcase how to leverage the CALVIN task indicators to learn RL agents with a sparse reward.
Citation
If you find the dataset or code useful, please cite:
@article{calvin21,
author = {Oier Mees and Lukas Hermann and Erick Rosete-Beas and Wolfram Burgard},
title = {CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks},
journal={arXiv preprint arXiv:2112.03227},
year = 2021,
}
License
MIT License
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