VFlash Enables Fast Visual Planning via Flash Primitive Decoding
Original title: VFlash: Fast, Accurate Visual Planning via Flash Primitive Decoding
VFlash predicts visual primitives jointly for 25x faster generation in robot trajectory tasks.
VFlash is a method for fast visual planning through Flash Primitive Decoding that exploits known structure of visual primitives to predict their coordinates jointly. A lightweight drafter generates an entire primitive in one forward pass supporting compact boxes and long trajectories. The model achieves lower prediction errors than autoregressive decoding across 2D and 3D robot tasks.
- 25x faster generation for 3D point motion forecasting
- 70.2% average success across four MolmoSpaces task suites
- Directly accepts structurally valid predictions
- Trains on annotated primitives without target-token agreement
From the original article
1 min read at huggingface.co
Models for physical AI tasks, such as robotics and autonomous driving, need not reason solely in language. They can use visual plans--points, bounding boxes, and trajectories--to represent spatial goals and anticipate motion. However, autoregressive vision-language models generate these plans one coordinate token at a time, making richer predictions increasingly costly.
By Yu-Cheng Chou, Shihao Wang, Jin Huang · huggingface.co
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