[2606.12499] Action-Effect Memory Pretraining for Robot Manipulation
[Submitted on 10 Jun 2026]
Title:Action-Effect Memory Pretraining for Robot Manipulation
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Abstract:We present AEM, an Action-Effect Memory pretraining framework for robot manipulation that learns compact temporal representations from vision-action history. Unlike prior robot representation pretraining methods that mainly focus on single-frame visual encoding, AEM targets the temporal nature of manipulation, where the current observation alone is often insufficient under partial observability. AEM models manipulation as an action-driven interaction process by interleaving visual and action features and applying masked modeling to recover missing content from incomplete histories, thereby learning action-conditioned state evolution. The Mamba-encoded output of the final vision token is used as a compact history representation, serving as the global context for decoding and downstream control. This design preserves a single-vector temporal bottleneck while keeping inference efficient. We evaluate AEM with Diffusion Policy and Flow Policy. AEM consistently improves manipulation performance in both simulation and real-world settings, outperforming baselines across clean scenes, cluttered and random scenes, and non-Markovian tasks. Ablation studies further show that history-aware pretraining surpasses single-frame pretraining and direct frame stacking, while reducing inference latency and computational cost.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.12499 [cs.RO]
(or arXiv:2606.12499v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12499
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Qiwei Liang [view email] [v1] Wed, 10 Jun 2026 13:58:14 UTC (2,101 KB)
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