# Learning Transferable Policies for Monocular Reactive MAV Control

arXiv: https://arxiv.org/abs/1608.00627 · HTML: https://mlsanity.com/paper/1608.00627/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

## Built on by (most cited first, 18 of 18)

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- [Programmatic Imitation Learning from Unlabeled and Noisy Demonstrations](https://mlsanity.com/paper/2303.01440/) (2023)
- [Concurrent Training Improves the Performance of Behavioral Cloning from Observation](https://mlsanity.com/paper/2008.01205/) (2020)
- [Adaptive Policy Transfer in Reinforcement Learning](https://mlsanity.com/paper/2105.04699/) (2021)
- [Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees](https://mlsanity.com/paper/2405.16668/) (2024)
- [Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances](https://mlsanity.com/paper/2511.03565/) (2025)
- [Offline Behavioral Data Selection](https://mlsanity.com/paper/2512.18246/) (2025)

## Abstract (arXiv)

The ability to transfer knowledge gained in previous tasks into new contexts is one of the most important mechanisms of human learning. Despite this, adapting autonomous behavior to be reused in partially similar settings is still an open problem in current robotics research. In this paper, we take a small step in this direction and propose a generic framework for learning transferable motion policies. Our goal is to solve a learning problem in a target domain by utilizing the training data in a different but related source domain. We present this in the context of an autonomous MAV flight using monocular reactive control, and demonstrate the efficacy of our proposed approach through extensive real-world flight experiments in outdoor cluttered environments.