让机器人在开放环境中自主修正计划和环境认知
SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
- 用符号模拟器与自适应记忆模块动态更新环境认知和行动方案
- 在扰动环境下计划成功率显著提升，符号世界完整性更好
- 适合需要长期规划的复杂机器人任务，如家庭服务、探索
近期研究尝试将视觉语言模型（VLMs）与依赖符号表示的经典规划器结合，以生成复杂具身任务的长时序计划。然而，在开放环境中，感知获得的符号表示常不完整，导致性能下降。为此，我们提出SCOPE——一种自适应符号规划框架，支持计划优化与符号世界的演化。该框架包含两个协同模块：符号执行模拟器（SESim）通过符号验证与真实执行反馈来优化计划并更新符号世界；自适应符号记忆（SASMem）则将反馈进一步提炼为演化知识，增强长时序规划与符号建模能力。在开放环境中的实验表明，SCOPE显著提升了符号世界的完整性、环境扰动下的计划成功率，以及跨任务的语义对齐与适应性。
原文摘要 · Abstract (English)
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.
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