# Multi-Robot Transfer Learning: A Dynamical System Perspective

arXiv: https://arxiv.org/abs/1707.08689 · HTML: https://mlsanity.com/paper/1707.08689/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Wide and Deep Graph Neural Network with Distributed Online Learning](https://mlsanity.com/paper/2107.09203/) (2021)
- [Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy](https://mlsanity.com/paper/2312.09436/) (2023)
- [Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint](https://mlsanity.com/paper/2404.05051/) (2024)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [Dimensionless Policies based on the Buckingham π Theorem: Is This a Good Way to Generalize Numerical Results?](https://mlsanity.com/paper/2307.15852/) (2023)
- [State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey](https://mlsanity.com/paper/2408.11822/) (2024)

## Abstract (arXiv)

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.