Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision
Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan,
Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg
Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution when robots are at risk or unable to make task progress. With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time. A central question is how to effectively allocate limited human attention to individual robots. Prior work addresses this in the single-robot, single-human setting. We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors. We present a fully implemented open-source IFL benchmark suite of GPU-accelerated Isaac Gym environments for the evaluation of IFL algorithms. We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation. We also perform 1000 trials of a physical block-pushing experiment with 4 ABB YuMi robot arms. Experiments suggest that the allocation of humans to robots significantly affects the performance of the fleet, and that our algorithm achieves up to 8.8x higher return on human effort than baselines.