Interactive Robot Learning from Verbal Correction
Huihan Liu Alice Chen Yuke Zhu Adith Swaminathan Andrey Kolobov Ching-An Cheng
The ability to learn and refine behavior after deployment has become ever
more important for robots as we design them to operate in unstructured
environments like households. In this work, we design a new learning system
based on large language model (LLM), OLAF, that allows everyday users to teach
a robot using verbal corrections when the robot makes mistakes, e.g., by saying
"Stop what you're doing. You should move closer to the cup." A key feature of
OLAF is its ability to update the robot's visuomotor neural policy based on the
verbal feedback to avoid repeating mistakes in the future. This is in contrast
to existing LLM-based robotic systems, which only follow verbal commands or
corrections but not learn from them. We demonstrate the efficacy of our design
in experiments where a user teaches a robot to perform long-horizon
manipulation tasks both in simulation and on physical hardware, achieving on
average 20.0% improvement in policy success rate. Videos and more results are
at https://ut-austin-rpl.github.io/olaf/