  We consider the task of autonomously unloading boxes from trucks using an
industrial manipulator robot. There are multiple challenges that arise: (1)
real-time motion planning for a complex robotic system carrying two articulated
mechanisms, an arm and a scooper, (2) decision-making in terms of what action
to execute next given imperfect information about boxes such as their masses,
(3) accounting for the sequential nature of the problem where current actions
affect future state of the boxes, and (4) real-time execution that interleaves
high-level decision-making with lower level motion planning. In this work, we
propose a planning, learning, and reasoning framework to tackle these
challenges, and describe its components including motion planning, belief space
planning for offline learning, online decision-making based on offline
learning, and an execution module to combine decision-making with motion
planning. We analyze the performance of the framework on real-world scenarios.
In particular, motion planning and execution modules are evaluated in
simulation and on a real robot, while offline learning and online
decision-making are evaluated in simulated real-world scenarios.
