2 papers
cs.RO2026
RoboSSM: Scalable In-context Imitation Learning via State-Space Models
Youngju Yoo, Jiaheng Hu, Yifeng Zhu +4
In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at dep…
cs.CV2025
BEEP3D: Box-Supervised End-to-End Pseudo-Mask Generation for 3D Instance Segmentation
Youngju Yoo, Seho Kim, Changick Kim
3D instance segmentation is crucial for understanding complex 3D environments, yet fully supervised methods require dense point-level annotations, resulting in substantial annotati…