With the rapid growth of computing powers and recent advances in deep
learning, we have witnessed impressive demonstrations of novel robot
capabilities in research settings. Nonetheless, these learning systems exhibit
brittle generalization and require excessive training data for practical tasks.
To harness the capabilities of state-of-the-art robot learning models while
embracing their imperfections, we present Sirius, a principled framework for
humans and robots to collaborate through a division of work. In this framework,
partially autonomous robots are tasked with handling a major portion of
decision-making where they work reliably; meanwhile, human operators monitor
the process and intervene in challenging situations. Such a human-robot team
ensures safe deployments in complex tasks. Further, we introduce a new learning
algorithm to improve the policy's performance on the data collected from the
task executions. The core idea is re-weighing training samples with
approximated human trust and optimizing the policies with weighted behavioral
cloning. We evaluate Sirius in simulation and on real hardware, showing that
Sirius consistently outperforms baselines over a collection of contact-rich
manipulation tasks, achieving an 8% boost in simulation and 27% on real
hardware than the state-of-the-art methods, with twice faster convergence and
85% memory size reduction. Videos and code are available at
https://ut-austin-rpl.github.io/sirius