外观
论文arXiv 2610.1141820 天前
技能组合中已学习技能的经验引导启动搜索
原文链接：Experience-Guided Initiation Search for Learned Skills in Skill Composition（arXiv 2610.11418 · 发布日期：2026-10-08）
Experience-Guided Initiation Search for Learned Skills in Skill Composition
EVIS针对新环境部署冻结已学习技能的起始配置搜索，结合历史经验与目标环境行为验证，并在单技能和两阶段操作任务上评估。
核心要点
- 新环境中部署冻结VLA等技能需可靠起始配置，单个技能成功不保证组合任务完成。
- EVIS用历史执行经验优先排序候选，并用目标环境行为验证候选是否仍有效。
- 在冻结VLA策略的单技能和两阶段操作任务中，EVIS减少平均目标环境查询。
- 小交互预算下，EVIS提升可靠候选发现，表明可降低部署交互成本。
验证范围与局限：验证范围为使用冻结VLA策略的单技能和两阶段操作任务。
英文摘要 (Abstract)
Deploying frozen learned skills, such as Vision-Language-Action (VLA) policies, in new environments requires identifying initiation configurations that support reliable execution. In skill composition, an initiation configuration affects not only the current skill but also the physical state passed to subsequent skills, so successful execution of an individual skill does not necessarily imply successful completion of the composed task. Estimating target-specific capability through extensive rollouts is costly in real-world deployment, while directly reusing historical experience can be unreliable under environment changes. We propose EVIS, an Experience-Guided and Behavior-Validated Initiation Search framework for discovering reliable initiation configurations under limited target interaction. EVIS uses historical execution experience to prioritize promising candidates and target-environment behavior to validate whether they remain effective. We evaluate EVIS on single-skill and two-stage manipulation tasks with frozen VLA policies. EVIS reduces mean target-environment queries and improves reliable candidate discovery under small interaction budgets. These results show that combining historical guidance with target-side behavioral validation can reduce the interaction cost of deploying frozen learned skills in new environments.
作者：Qixuan Li, Yanhong Zhao, Jincheng Yu
来源
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