  Robotic failure is all too common in unstructured robot tasks. Despite well
designed controllers, robots often fail due to unexpected events. How do robots
measure unexpected events? Many do not. Most robots are driven by the
senseplan- act paradigm, however more recently robots are working with a
sense-plan-act-verify paradigm. In this work we present a principled
methodology to bootstrap robot introspection for contact tasks. In effect, we
are trying to answer the question, what did the robot do? To this end, we
hypothesize that all noisy wrench data inherently contains patterns that can be
effectively represented by a vocabulary. The vocabulary is generated by
meaningfully segmenting the data and then encoding it. When the wrench
information represents a sequence of sub-tasks, we can think of the vocabulary
forming sets of words or sentences, such that each subtask is uniquely
represented by a word set. Such sets can be classified using statistical or
machine learning techniques. We use SVMs and Mondrian Forests to classify
contacts tasks both in simulation and in real robots for one and dual arm
scenarios showing the general robustness of the approach. The contribution of
our work is the presentation of a simple but generalizable semantic scheme that
enables a robot to understand its high level state. This verification mechanism
can provide feedback for high-level planners or reasoning systems that use
semantic descriptors as well. The code, data, and other supporting
documentation can be found at:
http://www.juanrojas.net/2017icra_wrench_introspection.
