Learning Transferable Policies for Monocular Reactive MAV Control
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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.
Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2016.
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Citing papers per year: 2018: 1; 2019: 3; 2020: 2; 2021: 1; 2022: 3; 2023: 3; 2024: 3; 2025: 2.
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- 2018Behavioral Cloning from Observation4 May 2018
- 2018Behavioral Cloning from Observationcited by 482
- 2019Recent Advances in Imitation Learning from Observationcited by 92
- 2019Imitation Learning from Video by Leveraging Proprioceptioncited by 23
- 2019Adversarial Imitation Learning from Incomplete Demonstrationscited by 16
- 2024A Survey of Imitation Learning Methods, Environments and Metricscited by 16
- 2022Adversarial Imitation Learning from Video using a State Observercited by 10
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- 2020Efficiently Guiding Imitation Learning Agents with Human Gazecited by 6
- 2023Programmatic Imitation Learning from Unlabeled and Noisy Demonstrationscited by 3
- 2020Concurrent Training Improves the Performance of Behavioral Cloning from Observationcited by 2
- 2024Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guaranteescited by 1
- 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advancescited by 0
12 most cited of 13 All 13 (JSON)
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- 2024Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees26 May 2024
- 2024A Survey of Imitation Learning Methods, Environments and Metrics30 Apr 2024
- 2023Imitation Learning from Observation with Automatic Discount Scheduling11 Oct 2023
- 2023Programmatic Imitation Learning from Unlabeled and Noisy Demonstrations2 Mar 2023
- 2022Adversarial Imitation Learning from Video using a State Observer1 Feb 2022
- 2020Concurrent Training Improves the Performance of Behavioral Cloning from Observation3 Aug 2020
- 2020Efficiently Guiding Imitation Learning Agents with Human Gaze28 Feb 2020
- 2019Recent Advances in Imitation Learning from Observation30 May 2019
- 2019Adversarial Imitation Learning from Incomplete Demonstrations29 May 2019
- 2019Imitation Learning from Video by Leveraging Proprioception22 May 2019
12 newest of 13 All 13 (JSON)
- 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
- 2024ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Taskscited by 20
- 2022A Survey on Reinforcement Learning Methods in Character Animationcited by 12
- 2022MLNav: Learning to Safely Navigate on Martian Terrainscited by 3
- 2021Adaptive Policy Transfer in Reinforcement Learningcited by 2
- 2024ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks9 Dec 2024
- 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review10 Aug 2023
- 2022MLNav: Learning to Safely Navigate on Martian Terrains9 Mar 2022
- 2022A Survey on Reinforcement Learning Methods in Character Animation7 Mar 2022
- 2021Adaptive Policy Transfer in Reinforcement Learning10 May 2021