The code presented here was used in: Task-Embedded Control Networks for Few-Shot Imitation Learning.
If you want to be able to re-run the experiments presented in the paper, you will need some of the dependencies from a paper that we compare against: One-Shot Visual Imitation Learning via Meta-Learning .
Follow these steps:
- First clone the fork of the gym repo found here, and switch to branch mil.
- You can now either install this, or just add the gym fork to your PYTHONPATH.
- Download the mil_sim_reach and mil_sim_push datsets from here. Unzip them to the datasets folder. Note: The data format here has been changed slightly in comparison to the original data from the MIL paper.
- (Optional) Run the integration test to make sure everything is set-up correctly.
To run the reaching task, run:
./tecnets_corl_results.sh sim_reachTo run the pushing task, run:
./tecnets_corl_results.sh sim_pushThis section is for people who wish to extend the framework.
The code in designed in a pipelined fashion, where there are a list of consumers that takes in a dictionary of inputs (from a previous consumer) and then outputs a combined dictionary of the inputs and outputs of that consumer. For example:
a = GeneratorConsumer(...)
b = TaskEmbedding(...)
c = MarginLoss(...)
d = Control(...)
e = ImitationLoss(...)
consumers = [a, b, c, d, e]
p = Pipeline(consumers)This allows the TecNet to be built in a modular way. For example, if one wanted to do use a prototypical loss rather than a margin loss, then one would only need to swap out one of these consumers.
@article{james2018task,
title={Task-Embedded Control Networks for Few-Shot Imitation Learning},
author={James, Stephen and Bloesch, Michael and Davison, Andrew J},
journal={Conference on Robot Learning (CoRL)},
year={2018}
}