ORI Anniversary Series – Seminar 1
Peter Stone, University of Texas (LARG) hosted by ORI Associate Professor Nick Hawes
“Machine Learning for Robot Locomotion: Grounded Simulation Learning and Adaptive Planner Parameter Learning”
Robust locomotion is one of the most fundamental requirements for autonomous mobile robots. With the widespread deployment of robots in factories, warehouses, and homes, it is tempting to think that locomotion is a solved problem. However for certain robot morphologies (e.g. humanoids) and environmental conditions (e.g. narrow passages), significant challenges remain.
This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot (sim-to-real). It then introduces Adaptive Planner Parameter Learning as a way of leveraging human input (learning from demonstration) towards making existing robot motion planners more robust, without losing their safety properties.
Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot, and Adaptive Planner Parameter Learning has led to efficient learning of robust navigation policies in highly constrained spaces.