  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.
