5 papers
RLDX-1 Technical Report
Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65
While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene underst…
A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning
Yeseung Kim, Nayoung Oh, Jun Park +2
We present a visuo-tactile data-collection system that generates temporally structured, contact-rich demonstrations for imitation learning. Conventional systems often decouple the…
C2F-Space: Coarse-to-Fine Space Grounding for Spatial Instructions using Vision-Language Models
Nayoung Oh, Dohyun Kim, Junhyeong Bang +2
Space grounding refers to localizing a set of spatial references described in natural language instructions. Traditional methods often fail to account for complex reasoning -- such…
DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization
Nayoung Oh, Jaehyeong Jang, Moonkyeong Jung +1
We aim to solve the problem of generating coarse-to-fine skills learning from demonstrations (LfD). To scale precision, traditional LfD approaches often rely on extensive fine-grai…
LINGO-Space: Language-Conditioned Incremental Grounding for Space
Dohyun Kim, Nayoung Oh, Deokmin Hwang +1
We aim to solve the problem of spatially localizing composite instructions referring to space: space grounding. Compared to current instance grounding, space grounding is challengi…