Visual Navigation Among Humans With Optimal Control as a Supervisor
Abstract
Real world visual navigation requires robots to
operate in unfamiliar, human-occupied dynamic environments.
Navigation around humans is especially difficult because it
requires anticipating their future motion, which can be quite
challenging. We propose an approach that combines learning-
based perception with model-based optimal control to navigate
among humans based only on monocular, first-person RGB
images. Our approach is enabled by our novel data-generation
tool, HumANav, that allows for photorealistic renderings of
indoor environment scenes with humans in them, which are
then used to train the perception module entirely in simulation.
Through simulations and experiments on a mobile robot, we
demonstrate that the learned navigation policies can anticipate
and react to humans without explicitly predicting future human
motion, generalize to previously unseen environments and human
behaviors, and transfer directly from simulation to reality. Videos
describing our approach and experiments, as well as a live demo
of HumANav are available on the project website.
operate in unfamiliar, human-occupied dynamic environments.
Navigation around humans is especially difficult because it
requires anticipating their future motion, which can be quite
challenging. We propose an approach that combines learning-
based perception with model-based optimal control to navigate
among humans based only on monocular, first-person RGB
images. Our approach is enabled by our novel data-generation
tool, HumANav, that allows for photorealistic renderings of
indoor environment scenes with humans in them, which are
then used to train the perception module entirely in simulation.
Through simulations and experiments on a mobile robot, we
demonstrate that the learned navigation policies can anticipate
and react to humans without explicitly predicting future human
motion, generalize to previously unseen environments and human
behaviors, and transfer directly from simulation to reality. Videos
describing our approach and experiments, as well as a live demo
of HumANav are available on the project website.
Research Areas
