mlsanityData to 13 Oct 2026

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

arXiv 1912.12204 ↗First posted 24 Dec 2019cs.RO cs.AI cs.LGcited by 24

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

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.

Key facts. Cited by 24 papers in the map (data through 13 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.

Builds on

14 of 34 references.

Closest ancestors · 2

Same direction, newest first

  1. 2018RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation7 Nov
  2. 2018One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks25 Oct

Other references · 12

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,030
  2. 2019A Comprehensive Survey on Transfer Learningcited by 1,157
  3. 2017End-to-end Driving via Conditional Imitation Learningcited by 480
  4. 2018A Survey on Deep Transfer Learningcited by 417
  5. 2017Overcoming Exploration in Reinforcement Learning with Demonstrationscited by 325
  6. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  7. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  8. 2018Meta-Learning: A Surveycited by 254
  9. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 225
  10. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 77
  11. 2016Unsupervised Perceptual Rewards for Imitation Learningcited by 64
  12. 2017Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learningcited by 50

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 7; 2024: 5; 2025: 2; 2026: 1.

24 papers.

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  1. 2023Federated Incremental Semantic Segmentationcited by 36
  2. 2021Multimodal Federated Learning on IoT Datacited by 32
  3. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
  4. 2023Federated Domain Generalization: A Surveycited by 25
  5. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  6. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  7. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  8. 2023FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systemscited by 14
  9. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  10. 2021Personalized Federated Learning of Driver Prediction Models for Autonomous Drivingcited by 6
  11. 2024Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigationcited by 4
  12. 2024Combining Federated Learning and Control: A Surveycited by 3
12 more
  1. 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integrationcited by 2
  2. 2023Federated Learning on Edge Sensing Devices: A Reviewcited by 1
  3. 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FLcited by 1
  4. 2025MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Graspingcited by 1
  5. 2026ForgeVLA: Federated Vision-Language-Action Learning without Language Annotationscited by 1
  6. 2021A Novel Optimized Asynchronous Federated Learning Frameworkcited by 0
  7. 2022A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networkscited by 0
  8. 2022A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Armscited by 0
  9. 2023Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computingcited by 0
  10. 2023Scene 3-D Reconstruction System in Scattering Mediumcited by 0
  11. 2024MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attentioncited by 0
  12. 2025Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challengescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2019Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
  2. 2019Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation
  3. 2025Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions
  4. 2025RecipeMasterLLM: Revisiting RoboEarth in the Era of Large Language Models ↗
  5. 2023IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
  6. 2026Federated Subspace Guided Vision-Language-Action Policy Distillation for Non-IID Multi-Robot Manipulation
  7. 2026An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
  8. 2022On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios