VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory

• URL: http://arxiv.org/abs/2603.04910v1
• Date: Thu, 05 Mar 2026 07:52:50 GMT
• Title: VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory
• Authors: Yuheng Lei, Zhixuan Liang, Hongyuan Zhang, Ping Luo,
• Abstract summary: VPWEM is a non-Markovian visuomotor policy equipped with working and episodic memories.<n>It exploits both short-term and episode-wide information for action generation with nearly constant memory and computation per step.
• Score: 31.464584758455356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condition on single-step observations or short-context histories, making them struggle with non-Markovian tasks that require long-term memory. Simply enlarging the context window incurs substantial computational and memory costs and encourages overfitting to spurious correlations, leading to catastrophic failures under distribution shift and violating real-time constraints in robotic systems. By contrast, humans can compress important past experiences into long-term memories and exploit them to solve tasks throughout their lifetime. In this paper, we propose VPWEM, a non-Markovian visuomotor policy equipped with working and episodic memories. VPWEM retains a sliding window of recent observation tokens as short-term working memory, and introduces a Transformer-based contextual memory compressor that recursively converts out-of-window observations into a fixed number of episodic memory tokens. The compressor uses self-attention over a cache of past summary tokens and cross-attention over a cache of historical observations, and is trained jointly with the policy. We instantiate VPWEM on diffusion policies to exploit both short-term and episode-wide information for action generation with nearly constant memory and computation per step. Experiments demonstrate that VPWEM outperforms state-of-the-art baselines including diffusion policies and vision-language-action (VLA) models by more than 20% on the memory-intensive manipulation tasks in MIKASA and achieves an average 5% improvement on the mobile manipulation benchmark MoMaRT. Code is available at https://github.com/HarryLui98/code_vpwem.

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