Article

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

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.

Part of the event: DGIST's LT-Mem Wins Best Paper at IROS 2026

This page provides a summary and source link only.