# Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data arXiv: https://arxiv.org/abs/1912.12204 ยท HTML: https://mlsanity.com/paper/1912.12204/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. - Builds on 14 of its 34 references that are in the map. - First posted on arXiv 24 Dec 2019. ## Placed in - Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/) ## Builds on (most cited first, 14 of 14) - [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017) - [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018) - [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017) - [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018) - [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017) - [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018) - [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017) - [RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation](https://mlsanity.com/paper/1811.02790/) (2018) - [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019) - [Unsupervised Perceptual Rewards for Imitation Learning](https://mlsanity.com/paper/1612.06699/) (2016) - [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017) - [One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks](https://mlsanity.com/paper/1810.11043/) (2018) ## Built on by (most cited first, 24 of 24) - [Federated Incremental Semantic Segmentation](https://mlsanity.com/paper/2304.04620/) (2023) - [Multimodal Federated Learning on IoT Data](https://mlsanity.com/paper/2109.04833/) (2021) - [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021) - [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023) - [Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles](https://mlsanity.com/paper/2103.03786/) (2021) - [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021) - [Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification](https://mlsanity.com/paper/2206.01748/) (2022) - [FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems](https://mlsanity.com/paper/2304.01534/) (2023) - [Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2306.16169/) (2023) - [Personalized Federated Learning of Driver Prediction Models for Autonomous Driving](https://mlsanity.com/paper/2112.00956/) (2021) - [Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation](https://mlsanity.com/paper/2411.04112/) (2024) - [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024) - [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024) - [Federated Learning on Edge Sensing Devices: A Review](https://mlsanity.com/paper/2311.01201/) (2023) - [A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL](https://mlsanity.com/paper/2401.00809/) (2024) - [MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping](https://mlsanity.com/paper/2507.10158/) (2025) - [ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations](https://mlsanity.com/paper/2605.07474/) (2026) - [A Novel Optimized Asynchronous Federated Learning Framework](https://mlsanity.com/paper/2111.09487/) (2021) - [A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks](https://mlsanity.com/paper/2201.09952/) (2022) - [A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms](https://mlsanity.com/paper/2209.03511/) (2022) - [Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing](https://mlsanity.com/paper/2312.00316/) (2023) - [Scene 3-D Reconstruction System in Scattering Medium](https://mlsanity.com/paper/2312.09005/) (2023) - [MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention](https://mlsanity.com/paper/2411.00472/) (2024) - [Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges](https://mlsanity.com/paper/2507.17903/) (2025) ## Abstract (arXiv) Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.