Real-Time Execution with Autoregressive Policies
A new research paper introduces a method for achieving real-time execution in autoregressive policies for Vision-Language-Action models. The approach involves adjusting the tokenization horizon and employing constrained decoding to guarantee strict latency bounds. This enables multi-trajectory decoding, leading to improved task completion speeds and outperforming equivalent flow-matching policies in both simulated and real-world environments. AI
IMPACT Enables faster and more responsive AI agents in real-world applications by improving autoregressive policy execution.