CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

Overview

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.

๐Ÿ’ป 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

๐Ÿ‹๏ธโ€โ™‚๏ธ 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

๐Ÿ–ผ๏ธ Sensory Observations

CALVIN supports a range of sensors commonly utilized for visuomotor control:

  1. Static camera RGB images - with shape 200x200x3.
  2. Static camera Depth maps - with shape 200x200x1.
  3. Gripper camera RGB images - with shape 200x200x3.
  4. Gripper camera Depth maps - with shape 200x200x1.
  5. Tactile image - with shape 120x160x2x3.
  6. Proprioceptive state - EE position (3), EE orientation in euler angles (3), gripper width (1), joint positions (7), gripper action (1).

๐Ÿ•น๏ธ 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:

  1. Absolute cartesian pose - EE position (3), EE orientation in euler angles (3), gripper action (1).
  2. Relative cartesian displacement - EE position (3), EE orientation in euler angles (3), gripper action (1).
  3. Joint action - Joint positions (7), gripper action (1).

๐Ÿ’ช 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., โ€œopen the drawer. . . pick up the blue block. . . now push the block into the drawer. . . now open the sliding doorโ€. 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

๐Ÿ’ฌ 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.

๐Ÿ“ˆ 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

Owner
Oier Mees
PhD Student at the University of Freiburg, Germany. Researcher in Machine Learning and Robotics.
Oier Mees
PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

IIM - Crowd Localization This repo is the official implementation of paper: Learning Independent Instance Maps for Crowd Localization. The code is dev

tao han 91 Nov 10, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
Deduplicating Training Data Makes Language Models Better

Deduplicating Training Data Makes Language Models Better This repository contains code to deduplicate language model datasets as descrbed in the paper

Google Research 431 Dec 27, 2022
Blender Python - Node-based multi-line text and image flowchart

MindMapper v0.8 Node-based text and image flowchart for Blender Mindmap with shortcuts visible: Mindmap with shortcuts hidden: Notes This was requeste

SpectralVectors 58 Oct 08, 2022
A python3 tool to take a 360 degree survey of the RF spectrum (hamlib + rotctld + RTL-SDR/HackRF)

RF Light House (rflh) A python script to use a rotor and a SDR device (RTL-SDR or HackRF One) to measure the RF level around and get a data set and be

Pavel Milanes (CO7WT) 11 Dec 13, 2022
ATAC: Adversarially Trained Actor Critic

ATAC: Adversarially Trained Actor Critic Adversarially Trained Actor Critic for Offline Reinforcement Learning by Ching-An Cheng*, Tengyang Xie*, Nan

Microsoft 41 Dec 08, 2022
Pytorch Lightning code guideline for conferences

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Pytorch Lightning 1k Jan 06, 2023
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
The code for paper "Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation" which is accepted by AAAI 2022

Contrastive Spatio Temporal Pretext Learning for Self-supervised Video Representation (AAAI 2022) The code for paper "Contrastive Spatio-Temporal Pret

8 Jun 30, 2022
Stacked Generative Adversarial Networks

Stacked Generative Adversarial Networks This repository contains code for the paper "Stacked Generative Adversarial Networks", CVPR 2017. Part of the

Xun Huang 241 May 07, 2022
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

Skyformer This repository is the official implementation of Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method (NeurIPS 2021).

Qi Zeng 46 Sep 20, 2022
PyTorch implementation of the wavelet analysis from Torrence & Compo

Continuous Wavelet Transforms in PyTorch This is a PyTorch implementation for the wavelet analysis outlined in Torrence and Compo (BAMS, 1998). The co

Tom Runia 262 Dec 21, 2022
Accelerated SMPL operation, commonly used in generate 3D human mesh, STAR included.

SMPL2 An enchanced and accelerated SMPL operation which commonly used in 3D human mesh generation. It takes a poses, shapes, cam_trans as inputs, outp

JinTian 20 Oct 17, 2022
Official PaddlePaddle implementation of Paint Transformer

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [Paddle Implementation] Update We have optimized the serial inference p

TianweiLin 284 Dec 31, 2022
Discovering and Achieving Goals via World Models

Discovering and Achieving Goals via World Models [Project Website] [Benchmark Code] [Video (2min)] [Oral Talk (13min)] [Paper] Russell Mendonca*1, Ole

Oleg Rybkin 71 Dec 22, 2022
SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab

CORNELLSASLAB SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab Instructions: This python code can be used to convert SAS out

2 Jan 26, 2022
A Comparative Framework for Multimodal Recommender Systems

Cornac Cornac is a comparative framework for multimodal recommender systems. It focuses on making it convenient to work with models leveraging auxilia

Preferred.AI 671 Jan 03, 2023
Ensembling Off-the-shelf Models for GAN Training

Vision-aided GAN video (3m) | website | paper Can the collective knowledge from a large bank of pretrained vision models be leveraged to improve GAN t

345 Dec 28, 2022
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning (CoRL 2021)

O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning Object-object Interaction Affordance Learning. For a given object-object int

Kaichun Mo 26 Nov 04, 2022
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

Unsupervised Depth Completion with Calibrated Backprojection Layers PyTorch implementation of Unsupervised Depth Completion with Calibrated Backprojec

80 Dec 13, 2022