VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory
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...
| Authors | Yuheng Lei, Zhixuan Liang, Hongyuan Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 March 2026 |
| Last updated at source | 23 July 2026 |
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