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Tongji University
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Quantizing VLAs for robots doesn't have to trash performance: DA-PTQ recovers near full-precision accuracy even at low bit-widths by explicitly minimizing kinematic drift during sequential control.
Revitalizing the latent edge-sensitivity of DINO with SAM's structural priors yields state-of-the-art open-vocabulary segmentation, especially in cluttered scenes.
Forget relying on immediate observations: Keyframe-Chaining VLA lets robots nail long-horizon tasks by remembering and chaining together only the *important* past states.
By intrinsically guiding action refinement through sparse imagination, SC-VLA achieves SOTA performance in robot manipulation tasks, outperforming existing methods by a significant margin in both simulation and real-world settings.
Topological data analysis reveals that structurally stable and compact soft prompts lead to better downstream performance, enabling a new loss function (TSLoss) that improves convergence and tuning performance.
