2019/12/24 by Boyi Liu, Liu, Boyi, Lujia Wang +6 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO) #Robotics and Automated Systems #Robotics and Sensor-Based Localization #cs.AI #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.1912.12204
arXiv admin note: substantial text overlap with arXiv:1909.00895
arxiv created 2019/12/24 · openalex publication_date 2019/12/24 · arxiv updated 2019/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.