Sparsh Gautam is a computer vision engineer at Tesla with expertise in machine vision, robotics, and industrial automation. His technical focus encompasses robotics system reliability, calibration methods, and deploying vision systems within manufacturing and process automation environments. Sparsh contributes to the broader computer vision and robotics research community as a peer reviewer for ECCV, IJCV, IEEE RAL, and IROS. His reviewing work spans conference proceedings and journal submissions, covering advances in robotic perception, sensor-fusion techniques and industrial automation.
A model that performs well in simulation is an important milestone. But it is not proof that a robot is ready for the real world.
The hardest challenges in Physical AI emerge when learned behaviors encounter changing environments, imperfect sensors, limited compute, and unexpected edge cases. This panel will explore what it takes to bridge the gap between training and deployment, including the engineering disciplines required to build reliable robotic systems.
Experts will discuss how teams are addressing challenges across perception, decision-making, and manipulation to create robots that can operate safely and predictably outside the lab.
