DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
This paper introduces DIM-WAM, a memory-augmented world-action model that integrates multi-scale historical context, local future dynamics, and global task progress, significantly improving success rates in long-horizon robot manipulation tasks. On RMBench, average success increases from 28.4% to 69.8%, and on real-world Franka tasks, stage success rises from 70.7% to 91.5%.
[2606.27677] DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
[Submitted on 26 Jun 2026]
Title:DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
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Abstract:World-action models have shown promising robot-manipulation performance by jointly predicting future visual states and actions. However, existing methods mainly rely on short-term history and short-horizon future prediction, which is insufficient for long-horizon tasks whose correct execution depends on earlier observations and task progress. Such temporally dependent tasks require effective use of complementary temporal information, including recent local context, cross-stage historical events, immediate future dynamics, and global task progress. To address long-term forgetting and poor awareness of the global task state, we introduce DiM-WAM, a memory-augmented world-action model that integrates multi-scale historical context, local future dynamics, and global task progress. The memory extracts compact visual event information from real observations, updates multiple memory banks through independent similarity-based merging, and then reads the bank-identity- and time-embedded long-term context to condition video and action denoising. A progress-supervision objective further encourages memory tokens to encode not only completed historical events but also the current task stage and its implications for the remaining task. On RMBench, DiM-WAM raises average success from 28.4% with LingBot-VA to 69.8%, exceeding the explicit-memory Mem-0 baseline at 42.0%. On four real-world Franka tasks, it improves average stage success from 70.7% to 91.5% and full-task success from 52.5% to 80.0%. Project page: this https URL{\texttt{this https URL}}.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.27677 [cs.RO]
(or arXiv:2606.27677v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.27677
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kai Wang [view email] [v1] Fri, 26 Jun 2026 03:17:39 UTC (1,978 KB)
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