Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
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 \textbf{A}utonomous \textbf{A}erial Manipulation via \textbf{Co}ntextual \textbf{Co}ntrastive Meta Reinforcement Learning (\textbf{\textit{Aco2...
| Authors | Lixuan Jin, Bingxuan Lan, Xinyi Bao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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