FLASH Policy uses sparse Legendre polynomial trajectory fitting and history-anchored flow matching to enable single-step inference for visuomotor control, reporting 31.4 ms per-episode latency and >=92% success on five simulated plus two real manipulation tasks.
arXiv preprint arXiv:2602.06698 , year=
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ECTraj enhances consistency models for multi-agent trajectory prediction via improved student-teacher supervision and conditional top-K generation, yielding faster inference and competitive accuracy on Argoverse 2.
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FLASH: Efficient Visuomotor Policy via Sparse Sampling
FLASH Policy uses sparse Legendre polynomial trajectory fitting and history-anchored flow matching to enable single-step inference for visuomotor control, reporting 31.4 ms per-episode latency and >=92% success on five simulated plus two real manipulation tasks.
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Enhancing Consistency Models for Multi-Agent Trajectory Prediction
ECTraj enhances consistency models for multi-agent trajectory prediction via improved student-teacher supervision and conditional top-K generation, yielding faster inference and competitive accuracy on Argoverse 2.