Last month, I had the opportunity to join a fantastic panel at the NYSE Robotics & AI Infrastructure event, focused on the evolving landscape of robotics software.
The discussion was led by David Vellante from theCUBE and also featured panelists: Brian Gerkey (Intrinsic), Sviat Dulianinov (Bright Machines), and Lindon Gao (Dyna Robotics). These are some of the most thoughtful leaders at the intersection of robotics and AI.
For me, sharing the panel with Brian Gerkey was especially meaningful. Brian is a true force, having pioneered the development of ROS and Open Robotics, which have become the de facto standard and backbone of robotics software worldwide.
During the panel, Brian and I talked about a notable trend: how robotics software is swinging back toward more monolithic architectures, driven in part by the rise of end-to-end foundation models. This shift opens up exciting new possibilities for autonomy, but it also brings up hard questions, especially around explainability. When black-box models are operating physical systems, understanding why something went wrong isn’t just useful — it's critical.
That’s where Roboto’s analytics platform comes in: by providing powerful tools for searching and analyzing robotics data, we help teams gain visibility and explainability into complex systems, making these emerging architectures safer and more reliable.
👇 You can watch the full panel video below and see the event program for other great discussions too.
Thanks to the amazing teams at the NYSE and theCUBE for getting us all together!
