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Results show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time and show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time.
This work presents NeurRAFT, a generative planning framework based on anchor-level flow matching and clearance-aware preference tuning that operates on compact anchor waypoints, and directly reshape the pretrained planner's distribution toward safer solutions without augmenting inference.
PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations, demonstrating that privileged latent dynamics alignment provides an effective training signal for improving anticipatory reasoning in VLA polic...
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