  Surgical robot automation has attracted increasing research interest over the
past decade, expecting its potential to benefit surgeons, nurses and patients.
Recently, the learning paradigm of embodied intelligence has demonstrated
promising ability to learn good control policies for various complex tasks,
where embodied AI simulators play an essential role to facilitate relevant
research. However, existing open-sourced simulators for surgical robot are
still not sufficiently supporting human interactions through physical input
devices, which further limits effective investigations on how the human
demonstrations would affect policy learning. In this work, we study
human-in-the-loop embodied intelligence with a new interactive simulation
platform for surgical robot learning. Specifically, we establish our platform
based on our previously released SurRoL simulator with several new features
co-developed to allow high-quality human interaction via an input device. We
showcase the improvement of our simulation environment with the designed new
features, and validate effectiveness of incorporating human factors in embodied
intelligence through the use of human demonstrations and reinforcement learning
as a representative example. Promising results are obtained in terms of
learning efficiency. Lastly, five new surgical robot training tasks are
developed and released, with which we hope to pave the way for future research
on surgical embodied intelligence. Our learning platform is publicly released
and will be continuously updated in the website:
https://med-air.github.io/SurRoL.
