# Battery-Aware Reinforcement Learning for Aggressive Quadrotor Flight

arXiv: https://arxiv.org/abs/2609.37316 · HTML: https://mlsanity.com/paper/2609.37316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control](https://mlsanity.com/paper/2310.09053/) (2023)
- [SkyDreamer: Interpretable End-to-End Vision-Based Drone Racing with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2510.14783/) (2025)
- [Range, Endurance, and Optimal Speed Estimates for Multicopters](https://mlsanity.com/paper/2109.04741/) (2021)
- [Agile Flight Emerges from Multi-Agent Competitive Racing](https://mlsanity.com/paper/2512.11781/) (2025)
- [Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games](https://mlsanity.com/paper/2506.02849/) (2025)
- [How to Model Your Crazyflie Brushless](https://mlsanity.com/paper/2603.05944/) (2026)
- [Accurate Control under Voltage Drop for Rotor Drones](https://mlsanity.com/paper/2503.09017/) (2025)
- [BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight](https://mlsanity.com/paper/2607.23867/) (2026)

## Abstract (arXiv)

Agile flight tasks such as drone racing and pursuit-evasion require strong acceleration and precise turns, but the available thrust changes as the battery discharges and voltage drops under load. Conservative command limits make this variation easier to tolerate, at the cost of unused performance. We investigate how learned controllers can use that additional thrust while retaining the flight controller's voltage compensation and rate control. Our training simulator couples an identified load-transient battery model to rotor dynamics and firmware saturation. The feedforward policy receives filtered voltage during both training and deployment. Controlled ablations distinguish the benefit of a larger thrust-command range from that of voltage information. On a 38 g Crazyflie Brushless, the resulting policy reduces circle tracking error by 49% relative to stock-authority RL at 3.84 m/s, while preserving easy-task precision. Mean 20-lap race time decreases from 106.22 s to 95.24 s. Compared with a voltage-blind policy with the same increased authority, hardware error and race time are lower by 15.3% and 4.5%, respectively. In simulation, replacing the policy's voltage input with a recording from a different battery condition worsens hard-circle tracking, with a smaller, voltage-dependent effect in racing. Together, these results show where a simple voltage input complements existing actuator compensation in aggressive learned flight.