Interactive multi-modal motion planning with Branch Model Predictive Control
This addresses motion planning for autonomous systems like vehicles and robots interacting with uncontrolled agents, but it is incremental as it builds on existing MPC and risk measure methods.
The paper tackled motion planning for autonomous robots and vehicles in the presence of uncontrolled agents with multimodal behaviors by proposing a branch Model Predictive Control framework that plans over feedback policies and uses coherent risk measures like CVaR to balance performance and robustness. The result demonstrated human-like behaviors in simulation and experiments, achieving a balance between safety and performance.
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.