Safety-Oriented Task Offloading for Human-Robot Collaboration - A Learning-Based Approach

In Human-Robot Collaboration scenarios, safety must be ensured by a risk management process that requires the execution of computationally-expensive perception models (e.g., based on computer vision) in real-time. However, robots usually have constrained hardware resources which hinder achieving responses in a timely manner, resulting in unsafe operations. Although Multi- access Edge Computing allows robots to offload complex tasks to servers on the edge in order to meet real-time requirements, this might not be always possible due to dynamic changes in the network that can cause congestion or failures. This work proposes a safety-based task offloading strategy to address this problem. The goal is to intelligently use edge resources to reduce delays in the risk management process and consequently enhance safety. More specifically, depending on safety and network metrics, a Reinforcement Learning (RL) solution is implemented to decide whether a less accurate model should run locally on the robot or a more complex one should run remotely on the edge. A third possibility is to reuse the last output, through temporal coherence verification. Experiments are performed in a simulated warehouse scenario where humans and robots have close interactions. Results show that the proposed RL solution outperforms the baselines in several aspects. The edge is used only when the network performance is good, reducing the number of failures (up to 47%). The latency is also adapted to the safety requirements (risk × latency reduced up to 48%), avoiding unnecessary congestion in the network in safe situations and letting other robots in hazardous situations to use the edge. Overall, the latency of the risk management process is largely reduced (up to 68 %) and this positively affects safety, which is enhanced (time in safe zone increased up to 4%).