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https://github.com/pathak22/zeroshot-imitation
[ICLR 2018] TensorFlow code for zero-shot visual imitation by self-supervised exploration
https://github.com/pathak22/zeroshot-imitation
deep-learning demonstration imitation manipulation navigation tensorflow zero-shot
Last synced: 29 days ago
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[ICLR 2018] TensorFlow code for zero-shot visual imitation by self-supervised exploration
- Host: GitHub
- URL: https://github.com/pathak22/zeroshot-imitation
- Owner: pathak22
- License: bsd-2-clause
- Created: 2018-04-19T17:10:59.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-05-30T21:48:00.000Z (over 6 years ago)
- Last Synced: 2024-12-08T00:12:23.120Z (about 1 month ago)
- Topics: deep-learning, demonstration, imitation, manipulation, navigation, tensorflow, zero-shot
- Language: Python
- Homepage:
- Size: 8.88 MB
- Stars: 202
- Watchers: 14
- Forks: 43
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Zero-Shot Visual Imitation ##
#### In ICLR 2018 [[Project Website]](https://pathak22.github.io/zeroshot-imitation/) [[Videos]](http://pathak22.github.io/zeroshot-imitation/index.html#demoVideos)
Deepak Pathak\*, Parsa Mahmoudieh\*, Guanghao Luo\*, Pulkit Agrawal\*, Dian Chen,
Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
University of California, Berkeley
This is the implementation for the [ICLR 2018 paper Zero Shot Visual Imitation](https://pathak22.github.io/zeroshot-imitation). We propose an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. The key insight is the intuition that, for most tasks, reaching the goal is more important than how it is reached.
@inproceedings{pathakICLR18zeroshot,
Author = {Pathak, Deepak and
Mahmoudieh, Parsa and Luo, Guanghao and
Agrawal, Pulkit and Chen, Dian and
Shentu, Yide and Shelhamer, Evan and
Malik, Jitendra and Efros, Alexei A. and
Darrell, Trevor},
Title = {Zero-Shot Visual Imitation},
Booktitle = {ICLR},
Year = {2018}
}
### 1) Installation and Usage
#### Requirements
```Shell
git clone -b master --single-branch https://github.com/pathak22/zeroshot-imitation.git
cd zeroshot-imitation/
# (1) Install requirements:
sudo apt-get install python-tk
virtualenv venv
source $PWD/venv/bin/activate
pip install --upgrade pip
pip install numpy
pip install -r src/requirements.txt
# (2) Install Caffe: http://caffe.berkeleyvision.org/install_apt.html
git clone https://github.com/BVLC/caffe.git
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install --no-install-recommends libboost-all-dev
cd caffe/ # edit Makefile.config
make all -j
make pycaffe
make test -j
make runtest -j
# Note: If you are using conda, then its easy:
# $ conda install -c conda-forge caffe
# $ conda install -c conda-forge opencv=3.2.0
```
#### Data setup
Data can be downloaded at [google drive link](https://drive.google.com/file/d/1pnX8gGqs5EQVjGy4Z6FZ5oZ9noDHlC_8/view). This is the same data as used in [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://ropemanipulation.github.io).
You will need the rope dataset from this download.
Then, download the AlexNet weights, bvlc_alexnet.npy from [here](https://www.cs.toronto.edu/~guerzhoy/tf_alexnet/)
- put rope data in data/datasets/rope9
- it is important to name it rope9!
- put bvlc_alexnet.npy in nets/bvlc_alexnet.npy
#### Training
```Shell
python -i train.py
# fwd_consist=True to turn foward consistency loss on,
# or leave it False for to just learn the inverse model
r = RopeImitator('name', fwd_consist=True)
# to train baseline, turn baseline_reg=True. note that fwd_consist should be turned on as well (historical accident)
r = RopeImitator('name', fwd_consist=True, baseline_reg=True)
# Restore old models, if any. default of model_name is just current model name
r.restore(iteration, model_name='name of old model')
# training
r.train(num_iters)
```
Note that the accuracies presented is not a good measure of real world performance. The purpose of forward consistency is to learn actions consistent with state transistions, which don't necessarily have to be the ground truth actions.
### 2) Other resources
- [Paper](https://pathak22.github.io/zeroshot-imitation/resources/iclr18.pdf)
- [Project Website](https://pathak22.github.io/zeroshot-imitation/)
- [Videos](http://pathak22.github.io/zeroshot-imitation/index.html#demoVideos)