# TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.01125 · HTML: https://mlsanity.com/paper/2503.01125/

## Key facts

- Cited by 3 papers in the map (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 27 of its 39 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## 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, 27 of 27)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [Regularizing Action Policies for Smooth Control with Reinforcement Learning](https://mlsanity.com/paper/2012.06644/) (2020)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [NeuroBEM: Hybrid Aerodynamic Quadrotor Model](https://mlsanity.com/paper/2106.08015/) (2021)
- [Data-Driven MPC for Quadrotors](https://mlsanity.com/paper/2102.05773/) (2021)
- [Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories](https://mlsanity.com/paper/1712.02402/) (2017)
- [Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop](https://mlsanity.com/paper/2012.03806/) (2020)
- [A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight](https://mlsanity.com/paper/2109.01365/) (2021)
- [Learning a Single Near-hover Position Controller for Vastly Different Quadcopters](https://mlsanity.com/paper/2209.09232/) (2022)
- [Multi-Task Reinforcement Learning for Quadrotors](https://mlsanity.com/paper/2412.12442/) (2024)
- [Aggressive Online Control of a Quadrotor via Deep Network Representations of Optimality Principles](https://mlsanity.com/paper/1912.07067/) (2019)
- [Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real](https://mlsanity.com/paper/2108.12869/) (2021)
- [Learning Agile Flights through Narrow Gaps with Varying Angles using Onboard Sensing](https://mlsanity.com/paper/2302.11233/) (2023)
- [Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision](https://mlsanity.com/paper/1612.00291/) (2016)
- [How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning](https://mlsanity.com/paper/2012.06656/) (2020)
- [Range, Endurance, and Optimal Speed Estimates for Multicopters](https://mlsanity.com/paper/2109.04741/) (2021)
- [User-Conditioned Neural Control Policies for Mobile Robotics](https://mlsanity.com/paper/2211.12181/) (2022)
- [Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness](https://mlsanity.com/paper/2207.03524/) (2022)
- [Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation](https://mlsanity.com/paper/2310.15846/) (2023)
- [Catch Planner: Catching High-Speed Targets in the Flight](https://mlsanity.com/paper/2302.04387/) (2023)

## Built on by (most cited first, 3 of 3)

- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
- [Multi-Task Reinforcement Learning of Drone Aerobatics by Exploiting Geometric Symmetries](https://mlsanity.com/paper/2602.10997/) (2026)
- [ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning](https://mlsanity.com/paper/2603.10715/) (2026)

## Abstract (arXiv)

Although acrobatic flight control has been studied extensively, one key limitation of the existing methods is that they are usually restricted to specific maneuver tasks and cannot change flight pattern parameters online. In this work, we propose a target-and-command-oriented reinforcement learning (TACO) framework, which can handle different maneuver tasks in a unified way and allows online parameter changes. Additionally, we propose a spectral normalization method with input-output rescaling to enhance the policy's temporal and spatial smoothness, independence, and symmetry, thereby overcoming the sim-to-real gap. We validate the TACO approach through extensive simulation and real-world experiments, demonstrating its capability to achieve high-speed circular flights and continuous multi-flips.