@article{hosseinkhani2026imlevla,
  title = {{IMLE-VLA}: Fast Single-Step Action Generation for Vision-Language-Action Policies},
  author = {Hosseinkhani, Kian and Peng, Qinhe and Shramko, George and Aghabozorgi, Mehran and Qian, Jianing and Engst, Tristan and Moazeni, Alireza and Jayaraman, Dinesh and Li, Ke},
  journal = {IROS},
  year = {2026},
  month = {Oct},
  pub_type = {conference},
  url = {https://arxiv.org/abs/2609.10915},
  abstract = {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 π0.5. 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, avoiding the mode collapse of naive regression heads while eliminating multi-step sampling entirely. When IMLE-VLA is applied to π0.5, it increases inference frequency 3.67x (55 Hz vs. 15 Hz), enabling up to 11x higher action throughput. On the 40-task LIBERO benchmark, IMLE-VLA achieves the highest average success rate (98.0\%) among all baselines while leading in inference frequency. Under the test-time perturbations of LIBERO-plus, IMLE-VLA retains π0.5's robustness while other baselines degrade sharply, confirming that the cIMLE head preserves generalization. Real-world experiments on a Franka Emika Panda across four tasks demonstrate smoother motion (2.2x to 3.0x lower jerk) and faster task completion, with IMLE-VLA outperforming π0.5 on every task and reducing average VLA inference time per episode by 3.9x to 6.6x.},
  url_project = {https://kianhk6.github.io/IMLE-VLA/},
  url_pdf = {/publication/hosseinkhani-2026-imlevla/hosseinkhani-2026-imlevla.pdf},
  url_arxiv = {https://arxiv.org/abs/2609.10915},
}