3 papers
cs.LG2026
Locality-Aware Redundancy Pruning for LLM Depth Compression
Vincent-Daniel Yun, Youngrae Kim, Woosang Lim +3
Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing…
cs.RO2026
Learning-augmented robotic automation for real-world manufacturing
Yunho Kim, Quan Nguyen, Taewhan Kim +2
Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental changes. Learning-based control…
cs.RO2025
Learning Fast, Tool aware Collision Avoidance for Collaborative Robots
Joonho Lee, Yunho Kim, Seokjoon Kim +2
Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over…