(Georgia Institute of Technology, 2011)
Lee, Jinhan; Kiser, Jeffrey F.; Bobick, Aaron F.; Thomaz, Andrea L.
We present a novel method for the visual detection of a
contingent response by a human to the stimulus of a robot
action. Contingency is de ned as a change in an agent's be-
havior within a speci c time window in direct response to
a signal from another agent; detection of such responses is
essential to assess the willingness and interest of a human
in interacting with the robot. Using motion-based features
to describe the possible contingent action, our approach as-
sesses the visual self-similarity of video subsequences cap-
tured before the robot exhibits its signaling behavior and
statistically models the typical graph-partitioning cost of
separating an arbitrary subsequence of frames from the oth-
ers. After the behavioral signal, the video is similarly ana-
lyzed and the cost of separating the after-signal frames from
the before-signal sequences is computed; a lower than typ-
ical cost indicates likely contingent reaction. We present a
preliminary study in which data were captured and analyzed
for algorithmic performance.
(Georgia Institute of Technology, 2011)
Gielniak, Michael J.; Thomaz, Andrea L.
Coupled degrees-of-freedom exhibit correspondence, in that
their trajectories in
uence each other. In this paper we
add evidence to the hypothesis that spatiotemporal corre-
spondence (STC) of distributed actuators is a component of
human-like motion. We demonstrate a method for making
robot motion more human-like, by optimizing with respect
to a nonlinear STC metric. Quantitative evaluation of STC
between coordinated robot motion, human motion capture
data, and retargeted human motion capture data projected
onto an anthropomorphic robot suggests that coordinating
robot motion with respect to the STC metric makes the
motion more human-like. A user study based on mimick-
ing shows that STC-optimized motion is (1) more often rec-
ognized as a common human motion, (2) more accurately
identi ed as the originally intended motion, and (3) mim-
icked more accurately than a non-optimized version. We
conclude that coordinating robot motion with respect to the
STC metric makes the motion more human-like. Finally, we
present and discuss data on potential reasons why coordi-
nating motion increases recognition and ability to mimic.
(Georgia Institute of Technology, 2009)
Thomaz, Andrea L.; Cakmak, Maya
A general learning task for a robot in a new environment is
to learn about objects and what actions/effects they afford.
To approach this, we look at ways that a human partner
can intuitively help the robot learn, Socially Guided Machine
Learning. We present experiments conducted with
our robot, Junior, and make six observations characterizing
how people approached teaching about objects. We show
that Junior successfully used transparency to mitigate errors.
Finally, we present the impact of “social” versus “nonsocial”
data sets when training SVM classifiers.