Our VITA laboratory is interested in democratizing robots that will co-exist with humans in a safe, efficient, trustworthy, and socially-aware way. Self-driving cars, delivery robots, or social robots are examples of such robots. To realize this future, we propose empowering robots with a type of cognition we call socially-aware AI, i.e., robots that can not only perceive human behavior but reason with social intelligence – the ability to make safe and consistent decisions in unconstrained crowded social scenes.
Our research tackles the 3 Pillars of a socially-aware AI system tailored for transportation applications:
Perception, Prediction, and Planning.
Multi-person human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
We propose a collaborative sampling scheme between the generator and discriminator for improved data generation. Guided by the discriminator, our approach refines generated samples through gradient-based optimization in the data (or feature / latent) space, shifting the generator distribution closer to the real data distribution.
Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. In this project, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). More details here.