Mario Sposato
Papers
2
Total Citations
82
H-Index
2
About
Mario Sposato is a robotics researcher whose work bridges intelligent perception and sustainable automation. His primary research areas include deep learning for robotic manipulation, energy-optimal motion planning, and simulation-based optimization for industrial robots. His most impactful contribution is a deep learning-based method for vision-guided robotic grasping of unknown objects (63 citations), which enables robots to autonomously identify and pick objects they have never seen before—a critical capability for flexible manufacturing and logistics. Sposato also developed a novel simulation tool integrated with Delmia Robotics that automatically computes energy-optimal motion parameters for industrial robots (19 citations), allowing engineers to minimize energy consumption by tuning joint speeds and accelerations along a given end-effector path. This work directly addresses the growing demand for greener, cost-effective automation. By combining cutting-edge AI with practical simulation tools, Sposato’s research advances both the intelligence and efficiency of robotic systems, making him a notable contributor to the fields of robotic grasping and sustainable industrial robotics.
Research Focus
Key Achievements
Top Papers
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