2 papers
cs.RO2026
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
Kana Miyamoto, Kanata Suzuki, Tetsuya Ogata
Imitation learning for robotic tasks has relied primarily on policies trained only on successful demonstrations, although failures are unavoidable during human data collection. Man…
cs.RO2026
Compact Task-Aligned Imitation Learning for Laboratory Automation
Kanata Suzuki, Hanon Nakamurama, Hanon Nakamura +2
Robotic laboratory automation has traditionally relied on carefully engineered motion pipelines and task-specific hardware interfaces, resulting in high design cost and limited fle…