Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking
  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.
