Title: Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking,
Abstract: Transfer learning has the potential to reduce the burden of data collection
and to decrease the unavoidable risks of the training phase. In this letter, we
introduce a multirobot, multitask transfer learning framework that allows a
system to complete a task by learning from a few demonstrations of another task
executed on another system. We focus on the trajectory tracking problem where
each trajectory represents a different task, since many robotic tasks can be
described as a trajectory tracking problem. The proposed multirobot transfer
learning framework is based on a combined $\mathcal{L}_1$ adaptive control and
an iterative learning control approach. The key idea is that the adaptive
controller forces dynamically different systems to behave as a specified
reference model. The proposed multitask transfer learning framework uses
theoretical control results (e.g., the concept of vector relative degree) to
learn a map from desired trajectories to the inputs that make the system track
these trajectories with high accuracy. This map is used to calculate the inputs
for a new, unseen trajectory. Experimental results using two different
quadrotor platforms and six different trajectories show that, on average, the
proposed framework reduces the first-iteration tracking error by 74% when
information from tracking a different single trajectory on a different
quadrotor is utilized.