Verifiable Learned Behaviors via Motion Primitive Composition:
  Applications to Scooping of Granular Media
Andrew Benton Eugen Solowjow Prithvi Akella
A robotic behavior model that can reliably generate behaviors from natural
language inputs in real time would substantially expedite the adoption of
industrial robots due to enhanced system flexibility. To facilitate these
efforts, we construct a framework in which learned behaviors, created by a
natural language abstractor, are verifiable by construction. Leveraging recent
advancements in motion primitives and probabilistic verification, we construct
a natural-language behavior abstractor that generates behaviors by synthesizing
a directed graph over the provided motion primitives. If these component motion
primitives are constructed according to the criteria we specify, the resulting
behaviors are probabilistically verifiable. We demonstrate this verifiable
behavior generation capacity in both simulation on an exploration task and on
hardware with a robot scooping granular media.