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

arXiv: https://arxiv.org/abs/1709.04543 · HTML: https://mlsanity.com/paper/1709.04543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration](https://mlsanity.com/paper/2505.03673/) (2025)
- [Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review](https://mlsanity.com/paper/2111.12146/) (2021)
- [Dimensionless Policies based on the Buckingham π Theorem: Is This a Good Way to Generalize Numerical Results?](https://mlsanity.com/paper/2307.15852/) (2023)
- [Accelerating Full Waveform Inversion By Transfer Learning](https://mlsanity.com/paper/2408.00695/) (2024)
- [State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey](https://mlsanity.com/paper/2408.11822/) (2024)

## Abstract (arXiv)

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 L1 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.