Fleet Supervisor Allocation: A Submodular Maximization Approach

In real-world scenarios, the data collected by robots in diverse and unpredictable environments is crucial for enhancing their perception and decision-making models. This data is predominantly collected under human supervision, particularly through imitation learning (IL), where robots learn complex tasks by observing human supervisors. However, the deployment of multiple robots and supervisors to accelerate the learning process often leads to data redundancy and inefficiencies, especially as the scale of robot fleets increases. Moreover, the reliance on teleoperation for supervision introduces additional challenges due to potential network connectivity issues. To address these issues in data collection, we introduce an Adaptive Submodular Allocation policy, ASA, designed for efficient human supervision allocation within multi-robot systems under uncertain connectivity conditions. Our approach reduces data redundancy by balancing the informativeness and diversity of data collection, and is capable of accommodating connectivity variances. We evaluate the effectiveness of ASA in simulations with 100 robots across four different environments and various network settings, including a real-world teleoperation scenario over a 5G network. We train and test our policy, ASA, and state-of-the-art policies utilizing NVIDIA’s Isaac Gym. Our results show that ASA enhances the return on human effort by up to $3.37\times$, outperforming current baselines in all simulated scenarios and providing robustness against connectivity disruptions.