9,173 research outputs found
Talking About Task Progress: Towards Integrating Task Planning and Dialog for Assistive Robotic Services
The use of service robots to assist ageing people in their own homes has the potential to allow people to maintain their independence, increasing their health and quality of life. In many assistive applications, robots perform tasks on people’s behalf that they are unable or unwilling to monitor directly. It is important that users be given useful and appropriate information about task progress. People being assisted in homes and other realworld environments are likely be engaged in other activities while they wait for a service, so information should also be presented in an appropriate, nonintrusive manner. This paper presents a human-robot interaction experiment investigatingwhat type of feedback people prefer in verbal updates by a service robot about distributed assistive services. People found feedback about time until task completion more useful than feedback about events in task progress or no feedback. We also discuss future research directions that involve giving non-expert users more input into the task planning process when delays or failures occur that necessitate replanning or modifying goals
Who am I talking with? A face memory for social robots
In order to provide personalized services and to
develop human-like interaction capabilities robots need to rec-
ognize their human partner. Face recognition has been studied
in the past decade exhaustively in the context of security systems
and with significant progress on huge datasets. However, these
capabilities are not in focus when it comes to social interaction
situations. Humans are able to remember people seen for a
short moment in time and apply this knowledge directly in
their engagement in conversation. In order to equip a robot with
capabilities to recall human interlocutors and to provide user-
aware services, we adopt human-human interaction schemes to
propose a face memory on the basis of active appearance models
integrated with the active memory architecture. This paper
presents the concept of the interactive face memory, the applied
recognition algorithms, and their embedding into the robot’s
system architecture. Performance measures are discussed for
general face databases as well as scenario-specific datasets
Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
A major challenge for the realization of intelligent robots is to supply them
with cognitive abilities in order to allow ordinary users to program them
easily and intuitively. One way of such programming is teaching work tasks by
interactive demonstration. To make this effective and convenient for the user,
the machine must be capable to establish a common focus of attention and be
able to use and integrate spoken instructions, visual perceptions, and
non-verbal clues like gestural commands. We report progress in building a
hybrid architecture that combines statistical methods, neural networks, and
finite state machines into an integrated system for instructing grasping tasks
by man-machine interaction. The system combines the GRAVIS-robot for visual
attention and gestural instruction with an intelligent interface for speech
recognition and linguistic interpretation, and an modality fusion module to
allow multi-modal task-oriented man-machine communication with respect to
dextrous robot manipulation of objects.Comment: 7 pages, 8 figure
Language-based sensing descriptors for robot object grounding
In this work, we consider an autonomous robot that is required
to understand commands given by a human through natural language.
Specifically, we assume that this robot is provided with an internal
representation of the environment. However, such a representation is unknown
to the user. In this context, we address the problem of allowing a
human to understand the robot internal representation through dialog.
To this end, we introduce the concept of sensing descriptors. Such representations
are used by the robot to recognize unknown object properties
in the given commands and warn the user about them. Additionally, we
show how these properties can be learned over time by leveraging past
interactions in order to enhance the grounding capabilities of the robot
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
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