  This paper presents a novel online framework for safe crowd-robot interaction
based on risk-sensitive stochastic optimal control, wherein the risk is modeled
by the entropic risk measure. The sampling-based model predictive control
relies on mode insertion gradient optimization for this risk measure as well as
Trajectron++, a state-of-the-art generative model that produces multimodal
probabilistic trajectory forecasts for multiple interacting agents. Our modular
approach decouples the crowd-robot interaction into learning-based prediction
and model-based control, which is advantageous compared to end-to-end policy
learning methods in that it allows the robot's desired behavior to be specified
at run time. In particular, we show that the robot exhibits diverse interaction
behavior by varying the risk sensitivity parameter. A simulation study and a
real-world experiment show that the proposed online framework can accomplish
safe and efficient navigation while avoiding collisions with more than 50
humans in the scene.
