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Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision

Paper recorded by Signals 4 on 2026-09-17 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-17 on arXiv · recorded by Signals 4 on 2026-09-18

Category: cs.AI · 人工智能 · first seen 2026-09-18

Abstract

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 al

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#2 most recent of 300 cs.AI papers we have recorded · ↑ newer: Coding Agents with an Obstacle-Aware Harness for Safe Robot Manipulati · ↓ older: FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
Cite this page: Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision: the #2 most recent of 300 cs.AI papers we have recorded (as of 2026-09-17). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/workspace-models-lightweight-robotic-memory-via-saliency-driven-supervision.html
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