# Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring

arXiv: https://arxiv.org/abs/2606.08249 · HTML: https://mlsanity.com/paper/2606.08249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 94 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://mlsanity.com/paper/2311.12495/) (2023)
- [Train Hard, Fight Easy: Robust Meta Reinforcement Learning](https://mlsanity.com/paper/2301.11147/) (2023)
- [TrackerBots: Autonomous Unmanned Aerial Vehicle for Real-Time Localization and Tracking of Multiple Radio-Tagged Animals](https://mlsanity.com/paper/1712.01491/) (2017)
- [ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains](https://mlsanity.com/paper/2308.08104/) (2023)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [COMPASS: Cooperative Multi-Agent Persistent Monitoring using Spatio-Temporal Attention Network](https://mlsanity.com/paper/2507.16306/) (2025)

## Abstract (arXiv)

Reliable wildlife monitoring is essential for ecology and conservation, yet many existing methods, such as tagging, capture, and close-range observation, can alter the very behaviors they aim to measure. Aerial robots offer a scalable alternative, which has shown promising performance in multiple studies. Nonetheless, existing approaches typically lack behavioral awareness, rely on fixed heuristics, or require real-world training data that are costly, impractical, and ethically difficult to obtain. As a result, there remains no general framework for adaptive drone-based monitoring that can both preserve ecological validity and scale across species, behaviors, and robotic platforms. In this study, we introduce a disturbance-aware reinforcement-learning-based framework for heterogeneous aerial robotic fleets that enables autonomous wildlife tracking while explicitly minimizing behavioral disruption. We couple a zoologically grounded simulation environment with fitted animal movement models derived from real trajectory statistics, and train control policies using a reward formulation that captures the trade-off between observation quality and disturbance risk. Across three species (pigeon, jackal, and spur-winged lapwing) with distinct ecologies and motion patterns and four increasingly strategic behavior models common in nature, the learned policies consistently surpassed currently used rule-based baselines and generalized across monitoring tasks, animal dynamics, and drone types. These results establish disturbance-aware learning as a viable foundation for non-invasive autonomous wildlife observation, opening a path towards scalable, ethically responsible, and scientifically reliable robotic monitoring in ecology and conservation.