Title: Adapting Everyday Manipulation Skills to Varied Scenarios,
Abstract: We address the problem of executing tool-using manipulation skills in
scenarios where the objects to be used may vary. We assume that point clouds of
the tool and target object can be obtained, but no interpretation or further
knowledge about these objects is provided. The system must interpret the point
clouds and decide how to use the tool to complete a manipulation task with a
target object; this means it must adjust motion trajectories appropriately to
complete the task. We tackle three everyday manipulations: scraping material
from a tool into a container, cutting, and scooping from a container. Our
solution encodes these manipulation skills in a generic way, with parameters
that can be filled in at run-time via queries to a robot perception module; the
perception module abstracts the functional parts for the tool and extracts key
parameters that are needed for the task. The approach is evaluated in
simulation and with selected examples on a PR2 robot.