Generating Language Corrections for Teaching Physical Control Tasks

Authors: Megha Srivastava (@meghabyte), Noah Goodman, and Dorsa Sadigh

This repository contains the code for the ICML 2023 paper "Generating Language Corrections for Teaching Physical Control Tasks". In this work we design and build CORGI, a model trained to generate language corrections for three diverse physical control tasks (drawing, steering, and joint movement). CORGI takes in as input a pair of student and expert trajectories, and then generates natural language corrections to help the student improve. To train CORGI, we collect over 2k crowdsourced corrections for pair-wise (student, expert) trajectories, and further augment the data with large language-model (LLM) assistance.

If you find this respository useful, please cite:

@InProceedings{corgi2023srivastava,
  title = 	 {Generating Language Corrections for Teaching Physical Control Tasks},
  author = 	 {Srivastava, Megha and Goodman, Noah and Sadigh, Dorsa},
  booktitle	=   {International Conference on Machine Learning (ICML)},
  year = 	 {2023},
}

Interface

We provide code for user study interfaces to aid reproducibility of our experiment. Specifically:

• interfaces/teaching-interface/ contains source code for the learning gain experiment for the Drawing task.
• interfaces/data-collection-interface/ contains source code for the our data collection process. It is currently set-up for the Drawing task, but easily adaptable to any task where the data type consists of trajectories (sequence of states/actions).

To run each interface, simple run python server.py, and direct your browser to http://localhost:8080/?username=username.

Data

We provide data used to train CORGI. Specifically:

• data/resources/ contains gif files corresponding to movements shown during crowdsourcing for entries in data.json
• data/data.json contains the raw crowdsourced data without any data augmentation. These include fun and metaphorical examples across the different physical control tasks, such as:
  • Drawing: "make picture narrower and the end curl like a musical note", "make the loop more like a sidewards sharks fin"
  • Steering: "be brave dont be afraid dont stop"
  • Movement: "softer landing needed", "be more fluent in your movements"

Model

Please contact megha@cs.stanford.edu, as the file is too large!