[Submitted on 23 Oct 2026]
Title:Tiny Recursive Policy: Recursive Latent Refinement for Visuomotor Control
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Abstract:Behaviour cloning has become a powerful approach for robotic manipulation, but many modern policies reconstruct their internal representation from a fixed observation window at each prediction and do not maintain persistent latent context throughout execution. We introduce Tiny Recursive Policy (TRP), a parameter-efficient behaviour-cloning policy that combines persistent temporal state with recursive latent refinement. At each control step, TRP repeatedly updates high- and low-level latent states using a shared computation module before decoding an action sequence, and propagates the resulting state to subsequent predictions. We evaluate TRP across LIBERO-100, MetaWorld ML45, and the LIBERO-Mem benchmarks. TRP performs competitively across multi-task and few-shot settings and shows substantial gains on long-horizon and context-dependent tasks, while using significantly fewer parameters than large generative baselines. Execution-time perturbations show that reliance on the persistent carry increases with temporal-context demand, while training with carry perturbations substantially improves robustness to both carry and visual corruptions. Ablations reveal a strong interaction between persistent state and recursive refinement: the benefit of carry depends strongly on the amount of within-step computation, and the resulting performance cannot be explained by parameter count alone.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.33288 [cs.RO]
(or arXiv:2610.33288v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.33288
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hakan Aktas [view email] [v1] Fri, 23 Oct 2026 20:54:54 UTC (340 KB)
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