Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision

AI Digest - ArXiv AI

Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision

Complex robotic manipulation tasks frequently require a long-term memory of past events and actions. As conditioning on full histories renders policies prone to spurious correlations and degrades performance, many approaches to policy memory involve compressing historical information through expensive VLM queries in-the-loop to process only task-salient information. In this paper, we propose an alternative approach in which computationally intensive VLM queries are made during train-time to lear

Source: ArXiv AI