Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision

The paper introduces the workspace token, a lightweight latent memory representation for robotic manipulation that captures task-relevant historical information. By training with VLM queries only during training, the token can be queried efficiently at deployment, replacing full observations. Experiments in simulation and on hardware show that policies using the workspace token solve memory-intensive tasks without in‑loop VLM reasoning and even outperform heavier approaches.

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• robotics
• multimodal