(Georgia Institute of Technology, 2016-05)
Park, Daehyung; Erickson, Zackory; Bhattacharjee, Tapomayukh; Kemp, Charles C.
Online detection of anomalous execution can be
valuable for robot manipulation, enabling robots to operate
more safely, determine when a behavior is inappropriate,
and otherwise exhibit more common sense. By using multiple
complementary sensory modalities, robots could potentially
detect a wider variety of anomalies, such as anomalous contact
or a loud utterance by a human. However, task variability
and the potential for false positives make online anomaly
detection challenging, especially for long-duration manipulation
behaviors. In this paper, we provide evidence for the value of
multimodal execution monitoring and the use of a detection
threshold that varies based on the progress of execution. Using
a data-driven approach, we train an execution monitor that
runs in parallel to a manipulation behavior. Like previous
methods for anomaly detection, our method trains a hidden
Markov model (HMM) using multimodal observations from
non-anomalous executions. In contrast to prior work, our
system also uses a detection threshold that changes based on
the execution progress. We evaluated our approach with haptic,
visual, auditory, and kinematic sensing during a variety of manipulation
tasks performed by a PR2 robot. The tasks included
pushing doors closed, operating switches, and assisting ablebodied
participants with eating yogurt. In our evaluations, our
anomaly detection method performed substantially better with
multimodal monitoring than single modality monitoring. It also
resulted in more desirable ROC curves when compared with
other detection threshold methods from the literature, obtaining
higher true positive rates for comparable false positive rates.
(Georgia Institute of Technology, 2016-05)
Park, Daehyung; Kim, You Keun; Erickson, Zackory; Kemp, Charles C.
General-purpose mobile manipulators have the
potential to serve as a versatile form of assistive technology.
However, their complexity creates challenges, including the risk
of being too difficult to use. We present a proof-of-concept
robotic system for assistive feeding that consists of a Willow
Garage PR2, a high-level web-based interface, and specialized
autonomous behaviors for scooping and feeding yogurt. As a
step towards use by people with disabilities, we evaluated our
system with 5 able-bodied participants. All 5 successfully ate
yogurt using the system and reported high rates of success
for the system’s autonomous behaviors. Also, Henry Evans, a
person with severe quadriplegia, operated the system remotely
to feed an able-bodied person. In general, people who operated
the system reported that it was easy to use, including Henry.
The feeding system also incorporates corrective actions designed
to be triggered either autonomously or by the user. In an
offline evaluation using data collected with the feeding system,
a new version of our multimodal anomaly detection system
outperformed prior versions.