Plan First, Act Later: Streaming Reasoning for Executable Memory in Robot Control

Long-horizon robot manipulation requires a robot to remember task-relevant information beyond its short observation window. Existing memory-augmented policies typically retrieve relevant information from history and reason over it at execution time, placing a heavy reasoning burden on the high-level model. To address this challenge, we propose PAL (Plan first, Act Later), a hierarchical memory framework for vision-language robot manipulation. It maintains a short-term visual buffer, a semantic-event memory, and an executable subtask_list. A high-level vision-language model (VLM) converts streaming visual observations into the subtask_list. At execution time, the VLM selects the currently relevant subtask from the subtask_list and passes it to a low-level vision-language-action (VLA) policy. To teach the model what to write to memory and when to update it, we introduce a two-stage data generation procedure that first expands sparse update supervision and then uses self-labeling with task-grounded verification to generate training data that better match the state distribution encountered during execution. We evaluate our approach on memory-dependent benchmarks and real-world manipulation tasks. On RoboMME, PAL achieves a 56.05% success rate, outperforming the strongest baseline, FrameSamp-Modul, at 44.51%. In real-world experiments across four manipulation tasks, reaches 60.83% overall success, surpassing MemER (45.00%) and FrameSamp-Modul (28.33%). These results show that proactively writing future-relevant subtasks into hierarchical memory improves long-horizon visual reasoning and robot control. Code and videos can be found on our website: https://qinghangliu.github.io/pal-webpage/