SCOPE：自适应符号规划框架应对开放环境长程任务

SESim 通过符号验证和实际执行反馈来 refine 行动计划和进化符号世界；SASMem 则将反馈蒸馏为可演化的符号知识。

在开放环境实验中，SCOPE 使符号世界完整性提升，在环境扰动下计划成功率提高，并增强了跨任务泛化能力。

SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments

Recent works have explored integrating Vision-Language Models (VLMs) with classical planners that rely on symbolic representations of planning problems to generate long-horizon plans for complex embodied tasks. However, in open-ended environments, these symbolic representations obtained from perception are often incomplete, leading to suboptimal performance. To address this, we introduce SCOPE, a self-adaptive symbolic planning framework that supports refining action plans and evolving the symbolic world, i.e., the symbolic representations of open-ended environments. SCOPE comprises two synergistic modules: a Symbolic Execution Simulator (SESim) that conducts symbolic validation and real execution of action plans, leveraging the feedback to refine the plans and evolve the symbolic world; and a Self-Adaptive Symbolic Memory (SASMem) that further distills feedback into evolving symbolic knowledge to enhance long-horizon planning and modeling of the symbolic world. Experiments in open-ended environments show that SCOPE significantly improves the completeness of the symbolic world, the success rate of plans under environment perturbations, and cross-task grounding and adaptability across diverse embodied scenarios.

This report is categorized under AITOP's "Models" desk. Key entities and technical topics under continuous tracking include SCOPE, 符号规划, 视觉语言模型, 智能体.

Source attribution: curated from arXiv cs.AI (@Yundaichuan Zhan, Minghe Gao, Zhongqi Yue, Wendong Bu, Wenqiao Zhang, Guoming Wang, Jisheng Dang, Juncheng Li, Siliang Tang, Yueting Zhuang).