@Inproceedings{IvanovicHarrisonEtAl2023,
  author = {Ivanovic, B. and Harrison, J. and Pavone, M.},
  title = {Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning},
  abstract = {Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e.g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world. Despite their advancements, however, the vast majority of prediction systems are specialized to a set of well-explored geographic regions or operational design domains, complicating deployment to additional cities, countries, or continents. Towards this end, we present a novel method for efficiently adapting behavior prediction models to new environments. Our approach leverages recent advances in meta-learning, specifically Bayesian regression, to augment existing behavior prediction models with an adaptive layer that enables efficient domain transfer via offline fine-tuning, online adaptation, or both. Experiments across multiple real-world datasets demonstrate that our method can efficiently adapt to a variety of unseen environments.},
  month = may,
  year = {2023},
  address = {London, UK},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  owner = {borisi},
  timestamp = {2023-01-18},
  url = {https://arxiv.org/abs/2209.11820}
}