2026
LLM-Guided Semantic Stereo Adaptive Visual Servoing for Precise Peg-In-Hole
ICRA 2026poster
Precision assembly tasks like peg-in-hole remain challenging for robotic manipulation. While visual servoing offers a robust framework, it depends heavily on accurate calibration and manual feature engineering. Learning-based methods, including vision-language models (VLMs), provide strong semantic …