Network Offloading Policies for Cloud Robotics: A Learning-Based Approach
Sandeep Chinchali, Apoorva Sharma, J. Michael Harrison, Amine Elhafsi, Daniel D. Kang, Evgenya Pergament, Eyal Cidon, Sachin Rajsekhar Katti, Marco Pavone · Autonomous Robots · 2019
Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and object detection.However, resource-constrained robots, like lowpower drones, often have insufficient on-board compute resources or power reserves to scalably run the most accurate, state-ofthe art neural network compute models.Cloud robotics allows mobile robots the benefit of offloading compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models.However, cloud robotics comes with a key, often understated cost: communicating with the cloud over congested wireless networks may result in latency or loss of data.In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications, which we measure experimentally.In this paper, we formulate a novel Robot Offloading Problem -how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning.In both simulations and hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1.3-2.3× of benchmark offloading strategies, allowing robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.