CoRe: Contrastive Recurrent State-Space Models

Related tags

Deep Learningml-core
Overview

CoRe: Contrastive Recurrent State-Space Models

This code implements the CoRe model and reproduces experimental results found in
Robust Robotic Control from Pixels using Contrastive Recurrent State-Space models
NeurIPS Deep Reinforcement Learning Workshop 2021
Nitish Srivastava, Walter Talbott, Martin Bertran Lopez, Shuangfei Zhai & Joshua M. Susskind
[paper]

cartpole

cheetah

walker

Requirements and Installation

Clone this repository and then execute the following steps. See setup.sh for an example of how to run these steps on a Ubuntu 18.04 machine.

  • Install dependencies.

    apt install -y libgl1-mesa-dev libgl1-mesa-glx libglew-dev \
            libosmesa6-dev software-properties-common net-tools unzip \
            virtualenv wget xpra xserver-xorg-dev libglfw3-dev patchelf xvfb ffmpeg
    
  • Download the DAVIS 2017 dataset. Make sure to select the 2017 TrainVal - Images and Annotations (480p). The training images will be used as distracting backgrounds. The DAVIS directory should be in the same directory as the code. Check that ls ./DAVIS/JPEGImages/480p/... shows 90 video directories.

  • Install MuJoCo 2.1.

    • Download MuJoCo version 2.1 binaries for Linux or macOS.
    • Unzip the downloaded mujoco210 directory into ~/.mujoco/mujoco210.
  • Install MuJoCo 2.0 (For robosuite experiments only).

    • Download MuJoCo version 2.0 binaries for Linux or macOS.
    • Unzip the downloaded directory and move it into ~/.mujoco/.
    • Symlink mujoco200_linux (or mujoco200_macos) to mujoco200.
    ln -s ~/.mujoco/mujoco200_linux ~/.mujoco/mujoco200
    
    • Place the license key at ~/.mujoco/mjkey.txt.
    • Add the MuJoCo binaries to LD_LIBRARY_PATH.
    export LD_LIBRARY_PATH=$HOME/.mujoco/mujoco200/bin:$LD_LIBRARY_PATH
    
  • Setup EGL GPU rendering (if a GPU is available).

    • To ensure that the GPU is prioritized over the CPU for EGL rendering
    cp 10_nvidia.json /usr/share/glvnd/egl_vendor.d/
    
    • Create a dummy nvidia directory so that mujoco_py builds the extensions needed for GPU rendering.
    mkdir -p /usr/lib/nvidia-000
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia-000
    
  • Create a conda environment.

    For Distracting Control Suite

    conda env create -f conda_env.yml
    

    For Robosuite

    conda env create -f conda_env_robosuite.yml
    

Training

  • The CoRe model can be trained on the Distracting Control Suite as follows:

    conda activate core
    MUJOCO_GL=egl CUDA_VISIBLE_DEVICES=0 python train.py --config configs/dcs/core.yaml 
    

The training artifacts, including tensorboard logs and videos of validation rollouts will be written in ./artifacts/.

To change the distraction setting, modify the difficulty parameter in configs/dcs/core.yaml. Possible values are ['easy', 'medium', 'hard', 'none', 'hard_bg'].

To change the domain, modify the domain parameter in configs/dcs/core.yaml. Possible values are ['ball_in_cup', 'cartpole', 'cheetah', 'finger', 'reacher', 'walker'].

  • To train on Robosuite (Door Task, Franka Panda Arm)

    • Using RGB image and proprioceptive inputs.
    conda activate core_robosuite
    MUJOCO_GL=egl CUDA_VISIBLE_DEVICES=0 python train.py --config configs/robosuite/core.yaml
    
    • Using RGB image inputs only.
    conda activate core_robosuite
    MUJOCO_GL=egl CUDA_VISIBLE_DEVICES=0 python train.py --config configs/robosuite/core_imageonly.yaml
    

Citation

@article{srivastava2021core,
    title={Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models}, 
    author={Nitish Srivastava and Walter Talbott and Martin Bertran Lopez and Shuangfei Zhai and Josh Susskind},
    journal={NeurIPS Deep Reinforcement Learning Workshop},
    year={2021}
}

License

This code is released under the LICENSE terms.

Owner
Apple
Apple
Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"

ON-LSTM This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree

Yikang Shen 572 Nov 21, 2022
Invert and perturb GAN images for test-time ensembling

GAN Ensembling Project Page | Paper | Bibtex Ensembling with Deep Generative Views. Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhan

Lucy Chai 93 Dec 08, 2022
Code for the paper: Sketch Your Own GAN

Sketch Your Own GAN Project | Paper | Youtube Our method takes in one or a few hand-drawn sketches and customizes an off-the-shelf GAN to match the in

677 Dec 28, 2022
Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems Paper Finding Semantic Bugs in File Systems with an Extensible Fuzzin

gts3.org (<a href=[email protected])"> 129 Dec 15, 2022
AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models

AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models Descrip

Angel de Paula 1 Jun 08, 2022
Efficient 3D human pose estimation in video using 2D keypoint trajectories

3D human pose estimation in video with temporal convolutions and semi-supervised training This is the implementation of the approach described in the

Meta Research 3.1k Dec 29, 2022
Repository for MDPGT

MD-PGT Repository for implementing and reproducing the results for the paper MDPGT: Momentum-based Decentralized Policy Gradient Tracking. Available E

Xian Yeow Lee 2 Dec 30, 2021
This repository contains all data used for writing a research paper Multiple Object Trackers in OpenCV: A Benchmark, presented in ISIE 2021 conference in Kyoto, Japan.

OpenCV-Multiple-Object-Tracking Python is version 3.6.7 to install opencv: pip uninstall opecv-python pip uninstall opencv-contrib-python pip install

6 Dec 19, 2021
FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data, a relatively complete set of integrated multi-source data download terminal software fast is developed. The softw

ChangChuntao 23 Dec 31, 2022
Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Hybrid-Supervised Object Detection System Object detection system trained by hybrid-supervision/weakly semi-supervision (HSOD/WSSOD): This project is

5 Dec 10, 2022
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Self-labelling via simultaneous clustering and representation learning πŸ†— πŸ†— πŸŽ‰ NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Yuki M. Asano 469 Jan 02, 2023
Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation) Download Synthia dataset The model uses

32 Sep 21, 2022
Example of semantic segmentation in Keras

keras-semantic-segmentation-example Example of semantic segmentation in Keras Single class example: Generated data: random ellipse with random color o

53 Mar 23, 2022
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

unfoldedVBA Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution This repository contains the Pytorch implementation of the unrolled

Yunshi HUANG 2 Jul 10, 2022
Official implementation of "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Figure 1: Performance of SegFormer-B0 to SegFormer-B5. Project page

NVIDIA Research Projects 1.4k Dec 31, 2022
An end-to-end regression problem of predicting the price of properties in Bangalore.

Bangalore-House-Price-Prediction An end-to-end regression problem of predicting the price of properties in Bangalore. Deployed in Heroku using Flask.

Shruti Balan 1 Nov 25, 2022
[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VITA lab at EPFL 125 Dec 23, 2022
The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

The Equalization Losses for Long-tailed Object Detection and Instance Segmentation This repo is official implementation CVPR 2021 paper: Equalization

Jingru Tan 129 Dec 16, 2022
An Industrial Grade Federated Learning Framework

DOC | Quick Start | δΈ­ζ–‡ FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure comput

Federated AI Ecosystem 4.8k Jan 09, 2023
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   Β·   Rayhane Mama   Β·   Ragavan Thurairatn

Rayhane Mama 144 Dec 23, 2022