ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration

Shizuo Tian; Haodong Luo; Yutong Li; Yuebing Song; Yunxin Liu; Yuanchun Li

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arXiv · 2610.06999

ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration

Shizuo Tian·Haodong Luo·Yutong Li·Yuebing Song·Yunxin Liu·Yuanchun Li

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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.

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Shizuo Tian, Haodong Luo, Yutong Li, Yuebing Song, Yunxin Liu, Yuanchun Li. 2026-10-04. ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration. https://arxiv.org/abs/2610.06999

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