# IROS 2026 Best Paper LT-Mem explained: using hierarchical spatiotemporal memory to solve robotic temporal forgetting

- Source: 机器人产业应用 · 微信公众号
- Published: 2026-11-02T23:30:00.000Z
- RoboHot: https://robohot.news/en/items/r5apvel8j4ov2866vppsbouqb
- Original: https://mp.weixin.qq.com/s?__biz=Mzk1Nzg1NTc2MQ==&mid=2247501197&idx=1&sn=a9c04c6e421d9f664a726ceab5aab36e&chksm=c278fc621ef82436e41354d3f1d51b908489dacf9c6403fd3915c3d0b5ac5d4d3c649db92ffc&scene=126&sessionid=1793719824#rd

## Summary

The paper LT-Mem from the APRL lab at Daegu Gyeongbuk Institute of Science and Technology (DGIST) in South Korea won the IROS 2026 Best Paper award. The work proposes a volatile-perception spatiotemporal memory framework that uses three memory layers—Live, Delta, and Meta—and three volatility-based update strategies—coverage, retention, and multi-hypothesis—to solve the temporal forgetting problem in which new observations overwrite old memories when a robot revisits the same space multiple times.