[Submitted on 4 Oct 2026]
Title:ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration
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Abstract:Rapid adaptation to a new environment requires a robot to acquire useful knowledge about local objects, states, and interactions from limited experience. Systems that combine a reasoning agent with a frozen vision-language-action model (VLA) can adapt through execution feedback and memory, making the choice of experience central to their effectiveness. Repeated practice of a target task may refine a familiar solution while leaving other interactions relevant to changed conditions untested. We introduce ProactiveVLA, which uses proactive environment exploration to acquire reusable knowledge for deployment-time adaptation. After completing an initial task, the agent allocates the remaining interaction budget to self-proposed goals covering object affordances, state-changing interactions, and compositions of interactions. It verifies execution outcomes and consolidates both task-directed and exploratory experience into memory that guides subsequent planning and control. ProactiveVLA outperforms the baselines under the same turn budget on LIBERO-Pro and RoboCasa365 Composite-Seen. On LIBERO-Pro Goal-T, with at most one VLA primitive invocation allowed during evaluation, ProactiveVLA completes 48% of instances, compared with 19% for the state-of-the-art task-refinement baseline.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.06999 [cs.RO]
(or arXiv:2610.06999v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.06999
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shizuo Tian [view email] [v1] Sun, 4 Oct 2026 08:47:50 UTC (840 KB)
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