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Order2Taskplan-pytorch

This is a PyTorch implementation of the Episodic memory module described in our IROS 2018 paper "Adaptive Task Planner for Performing Home Service Tasks in Cooperation with a Human". Please note that this repository contains only the implementation of the episodic memory module(the deep learning part) in the paper.

Input

  • A perceived object list:
    • An object in the list can be expressed in multiple words in natual language
  • User command(order):
    • Natural language instruction or order that an user gives to a robot.(optional)

Output

  • A robot behavior sequence(taskplan) with n behaviors:
    • subject1 verb1 object1 subject2 verb2 object2 ... subjectn verbn objectn
    • subjectt = objectt if verbt deals with only one object.

concept

Dataset

  • Total 50,000 input-output pairs (Train: 45,000, Test: 5,000)
  • Ten robot behaviors: grasp, throw, locate, release, move, pour, put, push, sprinkle, and squeeze
  • Every pair is related to one of these four scenarios:

scenarios

  • Example dataset

Requirements

  • Ubuntu 16.04+
  • Python 3.6+
  • Pytorch 0.3.1+
  • Torchtext
  • Numpy
  • Matplotlib

Installing Order2Taskplan-pytorch

Order2Taskplan requires Python 3.6 or higher. It also requires installing PyTorch 0.3.1+ (warnings occur on 0.4.0). Its other dependencies are listed in requirements.txt. CUDA is required.

Run the following commands to clone the repository and install Order2Taskplan:

git clone https://github.com/chickenbestlover/Order2Taskplan-pytorch.git
cd Order2Taskplan-pytorch; pip install -r requirements.txt

Training

  • Step 1: Train Order2Taskplan model:
CUDA_VISIBLE_DEVICES=0 python 1_train_order2taskplan_model.py
  • Step 2: Train Hallucination model(optional, required if you don't want the user command(order) input):
CUDA_VISIBLE_DEVICES=0 python 2_train_hallucination_model.py

Download and place them to checkpoint/best_model

Demo Video

  • This video was presented at IROS 2018.

Video Label

Citation

Please cite the IROS 2018 paper if you use Order2Taskplan in your work:

@inproceedings{lee2018taskplan,
  title={Adaptive Task Planner for Performing Home Service Tasks in Cooperation with a Human},
  author={Lee, Seung-Jae and Park, Jin-Man and Kim, Deok-Hwa and Kim, Jong-Hwan},
  booktitle={Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on},
  year={2018}
  organization={IEEE}
}

License

The pre-trained models and the codes are released for uncommercial use.

Please open an issue if you have questions.