SimpleMemVLA
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A Simple but Effective Native-Video Memory for Vision-Language-Action Models • 11 items • Updated • 1
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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.
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