IMLE-VLA: Fast Single-Step Action Generation for Vision-Language-Action Policies
Vision-language-action (VLA) policies leverage pretrained vision-language backbones to achieve strong cross-task generalization. A leading design couples this backbone with a dedicated continuous action head trained via diffusion or flow matching. However, such heads rely on iterative multi-step sampling, for example 10 Euler steps in $π_{0.5}$. This creates an inference bottleneck that produces stop-and-go movement in the robot and slower task completion. We introduce IMLE-VLA, which replaces the iterative action head with a single-step conditional generator trained via conditional Implicit Maximum Likelihood Estimation (cIMLE). The cIMLE objective promotes multimodal action coverage, av...
| Authors | Kian Hosseinkhani, Qinhe Peng, George Shramko et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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