SimpleMemVLA Dataset

This repository contains the training data for SimpleMemVLA, a vision-language-action model that processes timestamped video history as native context for long-horizon robotic manipulation. The datasets are provided in LeRobot v3 format and cover the benchmarks: RMBench, RoboMME, MIKASA-Robo, RoboMemArena, and LIBERO.

• Paper: SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
• Project page: Hugging Face collection
• Code: GitHub repository

The datasets include robot trajectories with per-frame sub-task annotations, suitable for training and evaluating the model described in the paper.