ResearchResearch paperRobotics & Embodied AI1 source · Oct 21, 2026

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

To address this challenge, we propose PAL (Plan first, Act Later), a hierarchical memory framework for vision-language robot manipulation.

Long-horizon robot manipulation requires a robot to remember task-relevant information beyond its short observation window.
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.
These results show that proactively writing future-relevant subtasks into hierarchical memory improves long-horizon visual reasoning and robot control.

[1]Plan First, Act Later: Streaming Reasoning for Executable Memory in Robot Control
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 07:27 AM
To address this challenge, we propose PAL (Plan first, Act Later), a hierarchical memory framework for vision-language robot manipulation.
Long-horizon robot manipulation requires a robot to remember task-relevant information beyond its short observation window.

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