Microsoft's UniSteer Enables Robot to Learn Bead Tasks in 66 Mins
机器之心 · wechat · 2026-09-02
Microsoft Research proposes UniSteer, a method to enable efficient online fine-tuning of Vision-Language-Action (VLA) models on real robots by inverting human corrections into noise-space supervision. It addresses the incompatibility between human actions and the noise space in flow-matching architectures by freezing the pre-trained VLA and training only a lightweight noise actor. Experiments show UniSteer improves success rates from 20% to 90% across four tasks in an average of 66 minutes, completing a complex bead-placing task with just two full demonstration trajectories.
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