  Ensuring safe, real-time motion planning in arbitrary environments requires a
robotic manipulator to avoid collisions, obey joint limits, and account for
uncertainties in the mass and inertia of objects and the robot itself. This
paper proposes Autonomous Robust Manipulation via Optimization with
Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon
trajectory planner and tracking controller framework for robotic manipulators
to address these challenges. ARMOUR first constructs a robust controller that
tracks desired trajectories with bounded error despite uncertain dynamics.
ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs
required to track any trajectory within a continuum of desired trajectories.
Finally, ARMOUR over-approximates the swept volume of the manipulator; this
enables one to formulate an optimization problem that can be solved in
real-time to synthesize provably-safe motions. This paper compares ARMOUR to
state of the art methods on a set of challenging manipulation examples in
simulation and demonstrates its ability to ensure safety on real hardware in
the presence of model uncertainty without sacrificing performance. Project
page: https://roahmlab.github.io/armour/.
