Eliot Xing
Eliot Xing is a PhD student in the Robotics Institute at Carnegie Mellon University, co-advised by Guanya Shi and Jean Oh. His research spans reinforcement learning, robot learning, and differentiable simulation of complex physical systems. He is a co-first author, alongside Pablo Ortega-Kral, of RIO, an open-source Python framework for flexible real-time robot I/O designed to enable cross-embodiment robot learning and policy deployment across diverse hardware platforms.
“CMU researchers open-sourced a middleware framework that cuts robot-to-robot porting time from weeks to under an hour, directly attacking the infrastructure tax that's slowing down every VLA deployment team in the field.”
Source→“Pablo Ortega-Kral & Eliot Xing — Carnegie Mellon University (equal first authors). Lead architects of RIO. Their work sits at the intersection of systems engineering and robot learning.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.