Interactive multi-modal motion planning with Branch Model Predictive Control

TL;DR

This paper introduces a branch Model Predictive Control framework for autonomous robots that accounts for multimodal reactive behaviors of uncontrolled agents, balancing safety and performance through scenario trees and risk measures.

Contribution

It presents a novel branch MPC approach that plans over feedback policies using scenario trees, incorporating risk measures like CVaR for improved robustness.

Findings

Successfully tested on autonomous vehicle tasks in simulation.

Demonstrated human-like behaviors balancing safety and performance.

Validated on quadruped robot motion planning in real experiments.

Abstract

Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered. Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors, which contains a discrete element. We propose a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent. In particular, a scenario tree is constructed from a finite set of policies of the uncontrolled agent, and the branch MPC solves for a feedback policy in the form of a trajectory tree, which shares the same topology as the scenario tree. Moreover, coherent risk measures such as the Conditional Value at Risk (CVaR) are used as a tuning knob to adjust the tradeoff between performance and robustness. The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments. The result demonstrates interesting human-like behaviors, achieving a balance between safety and performance.