Tech Briefs Magazine - January 2023 - 20

Robotics & Automation
Highly tangential
pre-swirled fluid
flow attempting
to enter the seal
a minimum, purely axial. While conventional
swirl brakes have only been shown
to reduce pre-swirl by up to 30 percent,
the RVR can reverse the direction of the
swirl, so that circumferential fluid velocity
flows in a direction counter to shaft rotaForced
vortex in each cavity
rotating in the opposite
direction to the fluid pre-swirl
The RVR attached to a damping seal. The RVR converts the destabilizing pre-swirled flow into a
stabilizing flow in the opposite direction. (Image: NASA)
tion. Thus, a classic detriment to rotating
machinery becomes an asset to ameliorate
vibration issues through the RVR. The
RVR is axially efficient, typically increasing
the axial length of a smooth annular
seal on the order of 10 to 12 percent. It
allows turbopumps and similar devices to
be made smaller, lighter, faster, and safer.
NASA is actively seeking licensees to
commercialize this technology. Please
contact NASA's Licensing Concierge at
Agency-Patent-Licensing@mail.nasa.gov
or call at 202-358-7432 to initiate licensing
discussions. Follow this link for more
information: https://technology.nasa.
gov/patent/MFS-TOPS-96.
Computational Model Enables Robots to Learn
New Concepts
The model allows robots to ask clarifying questions to soldiers.
Army Research Laboratory, Adelphi, MD
F
uture Army missions will have autonomous
agents, such as robots, embedded
in human teams making decisions in
the physical world. One major challenge
toward this goal is maintaining performance
when a robot encounters something
it has not previously seen; for example,
a new object or location. Robots
will need to be able to learn these novel
concepts on the fly in order to support
the team and the mission.
Researchers have created a computational
model for automated question
generation and learning. The model
enables a robot to ask effective clarification
questions based on its knowledge
of the environment and to learn from
the responses. This process of learning
through dialogue works for learning
new words, concepts, and even actions.
Researchers integrated this model into a
cognitive robotic architecture.
In previous research, the team conducted
an empirical study to explore
and model how humans ask questions
when controlling a robot. This led to the
creation of the Human-Robot Dialogue
Learning (HuRDL) corpus, which contains
labeled dialogue data that catego20
rizes
the form of questions that study
participants asked. The HuRDL corpus
serves as the empirical basis for the computational
model for automated question
generation.
The model uses a decision network,
which is a probabilistic graphical model
that enables a robot to represent world
knowledge from its various sensory modalities
including vision and speech. It
reasons over these representations to ask
the best questions to maximize its knowledge
about unknown concepts.
For example, if a robot is asked to pick
up some object that it has never seen before,
it might try to identify the object by
asking a question such as, " What color is
it? " or another question from the HuRDL
corpus.
The question generation model
was integrated into the Distributed Integrated
Affect Reflection
Cognition
(DIARC) robot architecture originating
from collaborators at Tufts University.
In a proof-of-concept demonstration
in a virtual Unity 3D environment, the
researchers showed a robot learning
through dialogue to perform a collaborative
tool organization task.
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While prior research on soldier-robot
dialogue enabled robots to interpret soldier
intent and carry out commands, there
are additional challenges when operating
in tactical environments. For example, a
command may be misunderstood due to
loud background noise or a soldier can refer
to a concept with which a robot is unfamiliar.
As a result, robots need to learn
and adapt on the fly if they are to keep up
with soldiers in these environments.
The ability to learn through dialogue
is beneficial to many types of language-enabled
agents, such as robots
and sensors, that can use this technology
to better adapt to novel environments.
Such technology can be employed on
robots in remote collaborative interaction
tasks such as reconnaissance and
search-and-rescue, or in co-located human-agent
teams performing tasks such
as transport and maintenance.
This research is different from existing
approaches to robot learning in that
the focus is on interactive human-like dialogue
as a means to learn. This kind of
interaction is intuitive for humans and
prevents the need to develop complex
interfaces to teach the robot. AnothTech
Briefs, January 2023

https://technology.nasa.gov/patent/MFS-TOPS-96 https://technology.nasa.gov/patent/MFS-TOPS-96 http://www.techbriefs.com

Tech Briefs Magazine - January 2023

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