RefineFly, a failure-aware RL post-training framework for end-to-end UAV-VLA policies, built on token-level proximal policy optimization (PPO), maintains dynamic failure memory within a two-stage scene curriculum to sustain learning from unresolved tasks as training shifts from empirical to balanced scene sampling.
Boxiong Wang, Hui Kang, Geng Sun et al.· 0 citations
This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing, and highlights opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.
Wei-Jie Yuan, Geng Sun, Jia-Cheng Wang et al.· IEEE Journal on Selected Top...· 0 citations
RecoverFly is proposed, a failure-aware RL post-training framework for end-to-end UAV-VLA policies that adapts token-level RL for stable optimization of grammar-constrained autoregressive UAV actions, revisits unresolved failure cases to strengthen corrective learning and sample utilization, and combines a two-stage lo...
Boxiong Wang, Hui Kang, Geng Sun et al.· 0 citations
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