SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
This work addresses the problem of incomplete symbolic representations in open-ended environments for embodied agents, offering a method to dynamically refine plans and world models.
SCOPE introduces a self-adaptive symbolic planning framework that refines action plans and evolves symbolic world representations using feedback from a symbolic execution simulator and self-adaptive memory, achieving significant improvements in plan success rate and cross-task adaptability 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.