Luca Monorchio
Papers
2
Total Citations
6
H-Index
2
About
Luca Monorchio is a robotics researcher focused on advancing autonomous navigation and manipulation in unstructured environments. His work primarily targets two critical challenges: obstacle detection for mobile humanoid robots and robotic grasping through machine learning. In his 2021 study on active exploration, Monorchio addressed the limitations of conventional LiDAR-based navigation, which often fails to detect small or occluded obstacles. By integrating active exploration strategies, he enhanced a mobile humanoid robot’s ability to perceive and avoid hazards beyond its base-mounted sensor’s field of view, improving safety in real-world settings. His 2022 research on transfer and continual supervised learning for robotic grasping introduced a novel approach to leveraging grasping features, enabling robots to adapt to new objects without forgetting previously learned skills. This work has implications for long-term deployment in dynamic environments. Though early in his career, with citations accumulating steadily, Monorchio’s contributions demonstrate a commitment to bridging perception and manipulation, laying groundwork for more resilient and adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Active Exploration for Obstacle Detection on a Mobile Humanoid Robot4 citations · 2021
- 2