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TurboVLA achieves 97.7% success in robotic manipulation while using less than 1 GB of VRAM, challenging the dominance of LLM-centric approaches.
SRDP achieves unprecedented stability and quality in robotic polishing by seamlessly integrating stage-awareness with roughness constraints, outperforming existing methods.
Achieve up to 1.28x faster VLA model inference for robotic manipulation without retraining, simply by merging visual tokens based on depth.
Monocular depth estimation can now run at 161 FPS on edge devices without sacrificing too much accuracy, thanks to a clever asynchronous architecture that reuses features from a foundation model.
