- September 23, 2026
- By Tom Ventsias
A four-legged robot can learn to run and recover from a shove through reinforcement learning, a form of artificial intelligence in which a system learns through trial and error by receiving rewards for useful behavior. But transfer that robot’s control system into a body with a different weight, shape or set of motors, and those skills can fall apart.
University of Maryland and George Mason University researchers have developed a more efficient approach. Their framework, the Morphology-aware FiLM Critic (MorFiC), teaches one robot to move via a “locomotion policy” and then applies it to others without retraining. A paper describing the work has been accepted to the 2026 Conference on Robot Learning in November.
“Our approach trains on a single robot in about two hours,” said the study’s lead author, Prakhar Mishra M.Eng. ’22. Comparable approaches can require tens or hundreds of hours because they train on many robot designs or use much larger models, he said.
Mishra led the project’s conceptual development, methodology, implementation, experimental design and analysis. George Mason computer science doctoral student Amir Hossain Raj contributed to the experimental implementation, robot deployment and evaluation.
UMD’s Dinesh Manocha, a Distinguished University Professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS), and George Mason Assistant Professor Xuesu Xiao supervised the work and provided technical guidance and manuscript review. Much of the UMD research took place in Manocha’s Geometric Algorithms for Modeling, Motion and Animation (GAMMA) Lab.
The researchers trained MorFiC on a single quadruped—a four-legged robot—rather than a collection of robot bodies, then tested the same policy on seven other robotic platforms. This “zero-shot” transfer required each machine to use what the system had already learned, without further training or adjustments.
Differences in mass, leg geometry, joint limits and motor strength change how a robot responds to commands. Hardware tests exposed practical challenges, including control delays, errors in estimating movement, slipping feet and uneven steps. One type of robot tested also moved more slowly and failed more often under the team’s current deployment setup.
MorFiC has broader limits as well. Transfer depended heavily on how physically similar the target robot was to the training model. The researchers suggest training the system on a small number of source robots as a next step—more than one, but far fewer than large collections of robot designs.