# Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots

arXiv: https://arxiv.org/abs/2411.15130 · HTML: https://mlsanity.com/paper/2411.15130/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [RoVerFly: Robust and Versatile Implicit Hybrid Control of Quadrotor-Payload Systems](https://mlsanity.com/paper/2509.11149/) (2025)
- [Finite Memory Belief Approximation for Optimal Control in Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2601.03132/) (2026)
- [WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning](https://mlsanity.com/paper/2604.08958/) (2026)
- [Robust Adversarial Policy Optimization Under Dynamics Uncertainty](https://mlsanity.com/paper/2604.10974/) (2026)
- [In-Distribution Imagination for Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2609.38673/) (2026)

## Abstract (arXiv)

Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions.