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arXiv · 1709.04543

Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking

Karime Pereida·Mohamed K. Helwa·Angela P. Schoellig
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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.

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Karime Pereida, Mohamed K. Helwa, Angela P. Schoellig. 2017-09-13. Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking. https://doi.org/10.1109/lra.2018.2795653

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