How Path Robotics uses AI to optimize robotic welding
Path Robotics is applying artificial intelligence to simplify and improve robotic welding, a process that has long posed challenges for manufacturers. In a recent episode of The Robot Report Podcast, co-founder and CEO Andy Lonsberry detailed how the companyâs AI-driven systems use real-time vision guidance to automatically identify the optimal welding path and adjust the robotâs movements accordingly.
AI-driven adaptive welding
Lonsberry explained that traditional robotic welding requires extensive manual programming and setup, making it difficult to adapt to variations in parts or positions. Path Robotics has developed a solution that uses AI to map the torchâs path and then guide the robot through the weld, continuously adjusting based on real-time visual feedback. This approach aims to maintain an optimal weld quality while reducing the need for human intervention.
âWeâre building adaptive, AI-driven robotic systems designed for real-world production environments,â Lonsberry said. His work focuses on applying physical AI to longstanding manufacturing challenges, moving beyond rigid automation toward flexible, self-correcting systems.
Expansion into shipbuilding
The Columbus, Ohio-based company is also deploying Boston Dynamicsâ Spot quadruped robots for mobile welding applications in shipbuilding. This marks a shift from stationary robotic arms to mobile platforms that can navigate complex, large-scale environments.

Podcast highlights and broader context
The episode, numbered 252, featured Lonsberry alongside Michael Yip, an assistant professor of electrical and computer engineering at UC San Diego and director of the Advanced Robotics and Controls Laboratory (ARCLab). Yipâs research focuses on data-efficient robot control and motion planning, with applications in surgical robotics.
Other news discussed in the episode included the future of humanoid robots in surgery, Agility Roboticsâ stock market debut, and regulatory frustrations with autonomous vehicles interfering with first responders.
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