  Commercial and industrial deployments of robot fleets at Amazon, Nimble, Plus
One, Waymo, and Zoox query remote human teleoperators 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. 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 propose Return on Human Effort (ROHE) as a new metric and Fleet-DAgger, a
family of IFL algorithms. We present an open-source IFL benchmark suite of
GPU-accelerated Isaac Gym environments for standardized evaluation and
development of IFL algorithms. We compare a novel Fleet-DAgger algorithm to 4
baselines with 100 robots in simulation. We also perform a physical
block-pushing experiment with 4 ABB YuMi robot arms and 2 remote humans.
Experiments suggest that the allocation of humans to robots significantly
affects the performance of the fleet, and that the novel Fleet-DAgger algorithm
can achieve up to 8.8x higher ROHE than baselines. See
https://tinyurl.com/fleet-dagger for supplemental material.
