データ効率の良いマルチロボット・マルチタスク転移学習による軌道追従
Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking
L1適応制御と反復学習制御を組み合わせ、別のロボットで別のタスクを少数回実演するだけで新しい軌道追従タスクを高精度に学習するフレームワークを提案し、2機のクアッドロータと6軌道で初回追従誤差を平均74%低減した。
著者: Karime Pereida, Mohamed K. Helwa, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
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.
