Deep learning-based grasp prediction models have become an industry standard
for robotic bin-picking systems. To maximize pick success, production
environments are often equipped with several end-effector tools that can be
swapped on-the-fly, based on the target object. Tool-change, however, takes
time. Choosing the order of grasps to perform, and corresponding tool-change
actions, can improve system throughput; this is the topic of our work. The main
challenge in planning tool change is uncertainty - we typically cannot see
objects in the bin that are currently occluded. Inspired by queuing and
admission control problems, we model the problem as a Markov Decision Process
(MDP), where the goal is to maximize expected throughput, and we pursue an
approximate solution based on model predictive control, where at each time step
we plan based only on the currently visible objects. Special to our method is
the idea of void zones, which are geometrical boundaries in which an unknown
object will be present, and therefore cannot be accounted for during planning.
Our planning problem can be solved using integer linear programming (ILP).
However, we find that an approximate solution based on sparse tree search
yields near optimal performance at a fraction of the time. Another question
that we explore is how to measure the performance of tool-change planning: we
find that throughput alone can fail to capture delicate and smooth behavior,
and propose a principled alternative. Finally, we demonstrate our algorithms on
both synthetic and real world bin picking tasks.Comment: 14 pages (including the cover page), 5 Figures, Technical Report,
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