[2605.23128] $π_0$-EqM: Equilibrium Matching for Closed-Loop Vision-Language-Action Control
[Submitted on 22 May 2026]
Title:$π_0$-EqM: Equilibrium Matching for Closed-Loop Vision-Language-Action Control
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Abstract:Currently, Vision-Language-Action (VLA) models have become the most adopted paradigm for robotic manipulation for its great potential for task generalization. While most generative flow-matching action decoders for VLA control are often deployed with fixed sampling horizons, limiting state-dependent compute and temporal reuse across control cycles. We present $\pi_0$-EqM, which replaces the flow-matching expert in $\pi_0$ with an Equilibrium Matching (EqM) decoder while leaving the upstream VLA stack unchanged. Under a matched 300-step budget, $\pi_0$-EqM improves RoboTwin average success from 40.4% to 50.2% across 19 tasks and remains competitive on LIBERO, with its clearest gain on LIBERO-10 (87.0%). Two threshold scans reveal a task-dependent non-monotonic relation between residual and success, which we term the stationarity--executability gap. The results suggest that inference depth in iterative VLA control is part of policy design and introduce an energy-based VLA perspective that may inform future work on composable action generation across tasks and embodiments.
Comments: Preprint. 5 pages, 3 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.23128 [cs.RO]
(or arXiv:2605.23128v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.23128
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Huanming Liu [view email] [v1] Fri, 22 May 2026 01:07:07 UTC (1,010 KB)
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