  We introduce Reactive Action and Motion Planner (RAMP), which combines the
strengths of sampling-based and reactive approaches for motion planning. In
essence, RAMP is a hierarchical approach where a novel variant of a Model
Predictive Path Integral (MPPI) controller is used to generate trajectories
which are then followed asynchronously by a local vector field controller. We
demonstrate, in the context of a table clearing application, that RAMP can
rapidly find paths in the robot's configuration space, satisfy task and
robot-specific constraints, and provide safety by reacting to static or
dynamically moving obstacles. RAMP achieves superior performance through a
number of key innovations: we use Signed Distance Function (SDF)
representations directly from the robot configuration space, both for collision
checking and reactive control. The use of SDFs allows for a smoother definition
of collision cost when planning for a trajectory, and is critical in ensuring
safety while following trajectories. In addition, we introduce a novel variant
of MPPI which, combined with the safety guarantees of the vector field
trajectory follower, performs incremental real-time global trajectory planning.
Simulation results establish that our method can generate paths that are
comparable to traditional and state-of-the-art approaches in terms of total
trajectory length while being up to 30 times faster. Real-world experiments
demonstrate the safety and effectiveness of our approach in challenging table
clearing scenarios. Videos and code are available at:
https://samsunglabs.github.io/RAMP-project-page/
