AIR-VLA+ introduces cascaded manipulation and movement decoders plus asymmetric MoE to decouple action scales in aerial manipulation, reporting 48.0 average score and 80.2% task completion gain over single-head baseline on AIR-VLA benchmark.
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Soft-labelling ordinal deep learning with binomial, beta, triangular, and exponential distributions improves KL and CPPD grading over one-hot baselines on knee X-rays.
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AIR-VLA+: Decoupling Movement and Manipulation via Cascaded Dual-Action Decoders with Asymmetric MoE for Aerial Robots
AIR-VLA+ introduces cascaded manipulation and movement decoders plus asymmetric MoE to decouple action scales in aerial manipulation, reporting 48.0 average score and 80.2% task completion gain over single-head baseline on AIR-VLA benchmark.