# Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning

arXiv: https://arxiv.org/abs/2606.08533 · HTML: https://mlsanity.com/paper/2606.08533/

## Key facts

- Cited by 1 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 10 of its 54 references that are in the map.
- First posted on arXiv 7 Jun 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, 10 of 10)

- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment](https://mlsanity.com/paper/2312.01058/) (2023)
- [π, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation](https://mlsanity.com/paper/2603.25038/) (2026)
- [Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.01522/) (2025)
- [Global End-Effector Pose Control of an Underactuated Aerial Manipulator via Reinforcement Learning](https://mlsanity.com/paper/2512.21085/) (2025)
- [DiffCoord: Differentiable Coordination for Distributed Multi-Agent Trajectory Optimization](https://mlsanity.com/paper/2509.01630/) (2025)
- [CrazyMARL: Decentralized Direct Motor Control Policies for Cooperative Aerial Transport of Cable-Suspended Payloads](https://mlsanity.com/paper/2509.14126/) (2025)
- [FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation](https://mlsanity.com/paper/2601.22686/) (2026)
- [Disentangled Multi-Context Meta-Learning: Unlocking robust and Generalized Task Learning](https://mlsanity.com/paper/2509.01297/) (2025)

## Built on by (most cited first, 1 of 1)

- [UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents](https://mlsanity.com/paper/2609.18326/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are increasingly being deployed in logistics, service robotics, and other real-world applications, creating a growing demand for autonomous payload acquisition and delivery. Existing approaches typically assume pre-attached payloads or rely on specialized grippers, leaving versatile end-to-end aerial delivery largely unresolved, where different payloads induce highly variable flight dynamics, requiring a single policy to adapt online without manual calibration or explicit system identification. To this end, we study Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning (textbftextitAco2), a fully autonomous aerial delivery setting in which a quadrotor equipped with a lightweight hook continuously picks up, transports, and delivers diverse handle-equipped objects between randomized locations, all without human intervention. First, we design a contextual observation encoder that infers a compact latent context from recent interaction history, enabling the policy to adapt online to payload-dependent dynamics. To further improve the quality of this context, we introduce a contrastive objective that structures the context embedding around task-relevant variations, improving generalization across diverse payloads without requiring explicit system identification. Trained entirely in simulation with extensive domain randomization, textitAco2 can be directly deployed on a physical quadrotor without real-world fine-tuning.