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Shrinking the entropic regularization parameter in Schr枚dinger Bridges unlocks a 3x speedup in visual navigation without sacrificing accuracy, finally making diffusion-based policies viable for real-time robotics.
Flow-matching VLAs can ditch 80% of their inference time *without* sacrificing performance, thanks to a clever self-distillation trick that compresses multi-step denoising into a single shot.
Robots can now catch dynamically moving objects with human-level dexterity, thanks to a shared autonomy framework that intelligently blends teleoperation with learned diffusion policies.
