提出ESCPE框架，让机器人在复杂环境长任务中更智能地导航与操作。
ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation
- 用时空融合构建持久3D空间记忆，支持无深度感知的持续感知
- 动态执行策略实现全局导航与局部操作协同，成功率达65.09%（已见）
- 适合长时序、无详细指导的室内机器人任务，减少冗余探索
在复杂室内环境中，协调导航与操作对具身AI至关重要。然而，随着任务时长增加，现有方法常因灾难性遗忘、空间不一致和执行僵化而失效。为此，我们提出ESCAPE（基于情景空间记忆与自适应执行策略的长时程移动操作框架），通过紧密耦合的感知-定位-执行流程运行。为实现鲁棒感知，ESCAPE采用时空融合映射模块，自回归构建无需深度信息的持久3D空间记忆；并引入记忆驱动的目标定位模块，生成精准交互掩码。为实现灵活动作，其自适应执行策略动态协调主动全局导航与被动局部操作，以捕捉机会目标。在ALFRED基准测试中，ESCAPE在带步骤指令的已见/未见环境中分别达到65.09%和60.79%的成功率。通过减少冗余探索，其路径长度加权指标显著提升，并在缺乏详细指导的长时任务中仍保持稳健性能（61.24%/56.04%）。
原文摘要 · Abstract (English)
Coordinating navigation and manipulation with robust performance is essential for embodied AI in complex indoor environments. However, as tasks extend over long horizons, existing methods often struggle due to catastrophic forgetting, spatial inconsistency, and rigid execution. To address these issues, we propose ESCAPE (Episodic Spatial Memory Coupled with an Adaptive Policy for Execution), operating through a tightly coupled perception-grounding-execution workflow. For robust perception, ESCAPE features a Spatio-Temporal Fusion Mapping module to autoregressively construct a depth-free, persistent 3D spatial memory, alongside a Memory-Driven Target Grounding module for precise interaction mask generation. To achieve flexible action, our Adaptive Execution Policy dynamically orchestrates proactive global navigation and reactive local manipulation to seize opportunistic targets. ESCAPE achieves state-of-the-art performance on the ALFRED benchmark, reaching 65.09% and 60.79% success rates in test seen and unseen environments with step-by-step instructions. By reducing redundant exploration, our ESCAPE attains substantial improvements in path-length-weighted metrics and maintains robust performance (61.24% / 56.04%) even without detailed guidance for long-horizon tasks.
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