The Robot Turned Its Past Into the Next Plan

MaP-WAM stores completed segments as language plus sparse visual context, converts that history into the next language and visual plan, and lets a progress-aware model execute each segment for an unknown duration. Because the executor sees the compact plan instead of the full growing history, its context and approximate inference latency stay fixed. The authors report 83.3% success on RMBench and 78.0% on real-robot tasks. The architecture separates planning-time memory from action-time execution rather than treating one expanding window as both.