OpenEQA: Embodied Question Answering in the Era of Foundation Models

[paper] [project] [dataset] [bibtex]

open-eqa-teaser.mp4

Abstract

We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory, exemplified by agents on smart glasses, or by actively exploring the environment, as in the case of mobile robots. We accompany our formulation with OpenEQA – the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset, we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol, we evaluate several state-of-the-art foundation models including GPT-4V, and find that they significantly lag behind human-level performance. Consequently, OpenEQA stands out as a straightforward, measurable, and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI, conversational agents, and world models.

Dataset

The OpenEQA dataset consists of 1600+ question answer pairs $(Q,A^*)$ and corresponding episode histories $H$.

The question-answer pairs are available in data/open-eqa-v0.json and the episode histories can be downloaded by following the instructions here.

Preview: A simple tool to view samples in the dataset is provided here.

Baselines and Automatic Evaluation

Installation

The code requires a python>=3.9 environment. We recommend using conda:

conda create -n openeqa python=3.9
conda activate openeqa
pip install -r requirements.txt
pip install -e .

Running baselines

Several baselines are implemented in openeqa/baselines. In general, baselines are run as follows:

# set an environment variable to your personal API key for the baseline
python openeqa/baselines/<baseline>.py --dry-run  # remove --dry-run to process the full benchmark

See openeqa/baselines/README.md for more details.

Running evaluations

Automatic evaluation is implemented with GPT-4 using the prompts found here and here.

# set the OPENAI_API_KEY environment variable to your personal API key
python evaluate-predictions.py <path/to/results/file.json> --dry-run  # remove --dry-run to evaluate on the full benchmark

License

OpenEQA is released under the MIT License.

Contributors

Arjun Majumdar*, Anurag Ajay*, Xiaohan Zhang*, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Sasha Sax, Aravind Rajeswaran

Citing OpenEQA

@inproceedings{majumdar2023openeqa,
  author={Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Sasha Sax, Aravind Rajeswaran},
  title={{OpenEQA: Embodied Question Answering in the Era of Foundation Models}},
  booktitle={{CVPR}},
  year={2024},
}