Offloading Robot Functionality

Offloading robot functionality involves shifting computationally intensive tasks from robots to more powerful external resources like edge servers or the cloud, aiming to improve performance, reduce energy consumption, and meet stringent latency requirements. Current research focuses on optimizing offloading decisions using techniques like deep reinforcement learning (e.g., DDPG, DDQN), meta-learning, and novel data compression methods to minimize communication overhead and maximize efficiency. This research area is crucial for enabling advanced robotic applications, particularly in autonomous driving and AI-generated content services, by addressing the limitations of onboard computing resources and improving the overall performance and reliability of robotic systems.