2 research outputs found
Watch-Bot: Unsupervised Learning for Reminding Humans of Forgotten Actions
We present a robotic system that watches a human using a Kinect v2 RGB-D
sensor, detects what he forgot to do while performing an activity, and if
necessary reminds the person using a laser pointer to point out the related
object. Our simple setup can be easily deployed on any assistive robot.
Our approach is based on a learning algorithm trained in a purely
unsupervised setting, which does not require any human annotations. This makes
our approach scalable and applicable to variant scenarios. Our model learns the
action/object co-occurrence and action temporal relations in the activity, and
uses the learned rich relationships to infer the forgotten action and the
related object. We show that our approach not only improves the unsupervised
action segmentation and action cluster assignment performance, but also
effectively detects the forgotten actions on a challenging human activity RGB-D
video dataset. In robotic experiments, we show that our robot is able to remind
people of forgotten actions successfully
Action Representations in Robotics: A Taxonomy and Systematic Classification
Understanding and defining the meaning of "action" is substantial for
robotics research. This becomes utterly evident when aiming at equipping
autonomous robots with robust manipulation skills for action execution.
Unfortunately, to this day we still lack both a clear understanding of the
concept of an action and a set of established criteria that ultimately
characterize an action. In this survey we thus first review existing ideas and
theories on the notion and meaning of action. Subsequently we discuss the role
of action in robotics and attempt to give a seminal definition of action in
accordance with its use in robotics research. Given this definition we then
introduce a taxonomy for categorizing action representations in robotics along
various dimensions. Finally, we provide a systematic literature survey on
action representations in robotics where we categorize relevant literature
along our taxonomy. After discussing the current state of the art we conclude
with an outlook towards promising research directions.Comment: 36 pages, 4 figures, 7 tables, submitted to the International Journal
of Robotics Research (IJRR