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.
@article{arxiv.2602.21013,
title = {Notes-to-Self: Scratchpad Augmented VLAs for Memory Dependent Manipulation Tasks},
author = {Sanjay Haresh and Daniel Dijkman and Apratim Bhattacharyya and Roland Memisevic},
journal= {arXiv preprint arXiv:2602.21013},
year = {2026}
}