C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache
Researchers have developed a new method called C$^3$ache to speed up the inference process for World Action Models (WAMs). WAMs are known for their strong generalization capabilities in robotics but are computationally expensive due to a multi-step denoising process. C$^3$ache addresses this by caching and reusing computation residuals across different inference chunks, achieving up to a 2.5x speedup without significantly impacting task success rates. AI
IMPACT Accelerates inference for robotic control models, potentially enabling more complex real-time applications.