https://github.com/danfeix/rpn

Code Repository for Regression Planning Networks
https://github.com/danfeix/rpn

machine-learning pybullet pytorch robotics robotics-simulation

Last synced: 5 days ago
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Code Repository for Regression Planning Networks

• Host: GitHub
• URL: https://github.com/danfeix/rpn
• Owner: danfeiX
• License: mit
• Created: 2019-10-26T22:09:39.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-18T21:17:40.000Z (almost 5 years ago)
• Last Synced: 2023-12-19T14:51:01.355Z (11 months ago)
• Topics: machine-learning, pybullet, pytorch, robotics, robotics-simulation
• Language: Python
• Homepage:
• Size: 3.42 MB
• Stars: 57
• Watchers: 4
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Regression Planning Networks

![preview](assets/rpn.png)

A reference implementation of the [Regression Planning Networks](https://arxiv.org/abs/1909.13072). 

This repo also includes the simulated environments

used in the paper: GridWorld and Kitchen3D. GridWorld is built on [gym-minigrid](https://github.com/maximecb/gym-minigrid).

Kitchen3D is simulated with [PyBullet](https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet). Most of the PyBullet utilities are adapted from Caelan Garrett's 

[ss-pybullet](https://github.com/caelan/ss-pybullet). The [Appendix](assets/RPN_Appendix.pdf) of the original paper is also included here for reference.

To cite the code, use bibtex:

```

@inproceedings{xu2019rpn,

  title={Regression Planning Networks},

  author={Xu, Danfei and Martín-Martín, Roberto and Huang, De-An and Zhu, Yuke and Savarese, Silvio and Fei-Fei, Li},

  booktitle={Thirty-third Conference on Neural Information Processing Systems (NeurIPS)},

  year={2019}

 }

```

![preview](assets/preview.gif)

## Installation and Setup

Requirement: Python >= 3.6

Note: The code DOES NOT work with Python 3.5

```

$ git clone https://github.com/danfeiX/rpn.git

$ git submodule update --init --recursive

$ pip install -r requirements.txt

$ cd rpn

$ mkdir checkpoints

$ mkdir data

```

## Kitchen3D

### Dataset

To reproduce the main experiments, first create a demonstration dataset with three ingredients and two dishes 

(`I=3`, `D=2`). 

```

$ python create_dataset_pb.py --problem pb_cook_meal_3i_2d_3m_iter \

    --dataset data/pb_cook_3i_2d_3m_10_s0.group \

    --num_episodes 10 --num_tasks -1 --seed 0 \

    --teleport --num_chunks 100 --num_workers 10

```

The entire process takes around 3 hours with 10 works (`--num_workers 10`) on a machine with i7-7700K CPU @ 4.20GHz.

 The dataset will be written to `data/pb_cook_3i_2d_3m_10_s0.group`. 

 

To create a smaller validation set, run:

```

$ python create_dataset_pb.py --problem pb_cook_meal_3i_2d_3m_iter \

    --dataset data/pb_cook_3i_2d_3m_1_s1.group \

    --num_episodes 1 --num_tasks -1 --seed 1 \

    --teleport --num_chunks 10 --num_workers 10

```

 

### Train

To train our full RPN model, run:

```

$ python train.py --dataset data/pb_cook_3i_2d_3m_10_s0.group \

    --testset data/pb_cook_3i_2d_3m_1_s1.group \

    --exp rpn \

    --run experiment \

    --batch_size 128 \

    --eval_batch_size 128 \

    --eval_freq 1 --save_freq 1 \

    --num_epoch 10 --config configs/pbv_rpn.json \

    --num_workers 1

```

Checkpoints will be written to `checkpoints/rpn/experiment/` each epoch. Both text and tensorboard event will be written

to `checkpoints/rpn/experiment/log/`.

#### Evaluation

To evaluate a trained model on the Kitchen3D task with `I=6` and `D=3`, run:

```

$ python eval_pb.py --restore_path checkpoints/rpn/experiment/ckpt_ep6.pt \

       --problem pb_cook_meal_6i_3d_6m_shuffle \

       --num_eval 1000 --seed 1 \

       --display

```

Aside: Our naming convention for the cooking tasks is `pb_cook_meal_i_d_m_`, where `` is the 

number of ingredients involved, `` is the number of dishes to cook, `` is the maximum number of ingredients that

can be placed on a plate (usually set to be equal to ``), and `` is whether to shuffle the list of 

all possible tasks under the current task specification.