# ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning

arXiv: https://arxiv.org/abs/2603.10715 · HTML: https://mlsanity.com/paper/2603.10715/

## 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 23 references that are in the map.
- First posted on arXiv 11 Mar 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)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Whole-Body Control Through Narrow Gaps From Pixels To Action](https://mlsanity.com/paper/2409.00895/) (2024)
- [Learning Agile Flights through Narrow Gaps with Varying Angles using Onboard Sensing](https://mlsanity.com/paper/2302.11233/) (2023)
- [Human-Aware Physical Human-Robot Collaborative Transportation and Manipulation with Multiple Aerial Robots](https://mlsanity.com/paper/2210.05894/) (2022)
- [Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.16720/) (2024)
- [Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load](https://mlsanity.com/paper/2501.18802/) (2025)
- [Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors](https://mlsanity.com/paper/2209.11097/) (2022)
- [Reactive Aerobatic Flight via Reinforcement Learning](https://mlsanity.com/paper/2505.24396/) (2025)
- [AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation](https://mlsanity.com/paper/2310.15050/) (2023)
- [Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.01522/) (2025)
- [TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learning](https://mlsanity.com/paper/2503.01125/) (2025)
- [FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning](https://mlsanity.com/paper/2508.09797/) (2025)
- [PolyFly: Polytopic Optimal Planning for Collision-Free Cable-Suspended Aerial Payload Transportation](https://mlsanity.com/paper/2510.15226/) (2025)

## Abstract (arXiv)

Agile maneuvering of the quadrotor cable-suspended system is significantly hindered by its non-smooth hybrid dynamics. While model-free Reinforcement Learning (RL) circumvents explicit differentiation of complex models, achieving attitude-constrained or inverted flight remains an open challenge due to the extreme reward sparsity under strict orientation requirements. This paper presents ASTER, a robust RL framework that achieves, to our knowledge, the first successful autonomous inverted flight for the cable-suspended system. We propose hybrid-dynamics-informed state seeding (HDSS), an initialization strategy that back-propagates target configurations through physics-consistent kinematic inversions across both taut and slack cable phases. HDSS enables the policy to discover aggressive maneuvers that are unreachable via standard exploration. Extensive simulations and real-world experiments demonstrate remarkable agility, precise attitude alignment, and robust zero-shot sim-to-real transfer across complex trajectories.