[Submitted on 10 Sep 2026]
Title:IMLE-VLA: Fast Single-Step Action Generation for Vision-Language-Action Policies
View a PDF of the paper titled IMLE-VLA: Fast Single-Step Action Generation for Vision-Language-Action Policies, by Kian Hosseinkhani (1) and 12 other authors
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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 $\pi_{0.5}$. 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, avoiding the mode collapse of naive regression heads while eliminating multi-step sampling entirely. When IMLE-VLA is applied to $\pi_{0.5}$, 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 $\pi_{0.5}$'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 $\pi_{0.5}$ on every task and reducing average VLA inference time per episode by 3.9x to 6.6x. Videos and code are available at this https URL
Comments: 8 pages, 5 figures, 5 tables. Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. Project page: this https URL
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.2.9; I.2.6
Cite as: arXiv:2609.10915 [cs.RO]
(or arXiv:2609.10915v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.10915
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kian Hosseinkhani [view email] [v1] Thu, 10 Sep 2026 00:00:32 UTC (2,059 KB)
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