Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction

TL;DR

This paper introduces a risk-sensitive control framework for crowd-robot interaction that combines probabilistic human trajectory forecasting with model predictive control, enabling safe and adaptable navigation in crowded environments.

Contribution

It presents a modular approach integrating multimodal trajectory prediction with risk-sensitive control, allowing real-time safe navigation with adjustable risk preferences.

Findings

Successfully navigates safely among 50+ humans in simulation and real-world tests.

Demonstrates diverse robot behaviors by tuning risk sensitivity.

Achieves collision-free, efficient crowd navigation.

Abstract

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.