Notes-to-Self: Scratchpad Augmented VLAs for Memory Dependent Manipulation Tasks

• URL: http://arxiv.org/abs/2602.21013v1
• Date: Tue, 24 Feb 2026 15:30:55 GMT
• Title: Notes-to-Self: Scratchpad Augmented VLAs for Memory Dependent Manipulation Tasks
• Abstract summary: We show a way to impart both spatial and temporal memory to a vision-language-action (VLA) model by incorporating a language scratchpad.<n>We evaluate this approach on a split of memory-dependent tasks from the ClevrSkills environment, on MemoryBench, and on a challenging real-world pick-and-place task.
• Score: 9.55115186979077
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many dexterous manipulation tasks are non-markovian in nature, yet little attention has been paid to this fact in the recent upsurge of the vision-language-action (VLA) paradigm. Although they are successful in bringing internet-scale semantic understanding to robotics, existing VLAs are primarily "stateless" and struggle with memory-dependent long horizon tasks. In this work, we explore a way to impart both spatial and temporal memory to a VLA by incorporating a language scratchpad. The scratchpad makes it possible to memorize task-specific information, such as object positions, and it allows the model to keep track of a plan and progress towards subgoals within that plan. We evaluate this approach on a split of memory-dependent tasks from the ClevrSkills environment, on MemoryBench, as well as on a challenging real-world pick-and-place task. We show that incorporating a language scratchpad significantly improves generalization on these tasks for both non-recurrent and recurrent models.

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