17,941 research outputs found
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
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
A robot that can carry out a natural-language instruction has been a dream
since before the Jetsons cartoon series imagined a life of leisure mediated by
a fleet of attentive robot helpers. It is a dream that remains stubbornly
distant. However, recent advances in vision and language methods have made
incredible progress in closely related areas. This is significant because a
robot interpreting a natural-language navigation instruction on the basis of
what it sees is carrying out a vision and language process that is similar to
Visual Question Answering. Both tasks can be interpreted as visually grounded
sequence-to-sequence translation problems, and many of the same methods are
applicable. To enable and encourage the application of vision and language
methods to the problem of interpreting visually-grounded navigation
instructions, we present the Matterport3D Simulator -- a large-scale
reinforcement learning environment based on real imagery. Using this simulator,
which can in future support a range of embodied vision and language tasks, we
provide the first benchmark dataset for visually-grounded natural language
navigation in real buildings -- the Room-to-Room (R2R) dataset.Comment: CVPR 2018 Spotlight presentatio
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
Continuous Interaction with a Virtual Human
Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access
Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process
Constructing a smart wheelchair on a commercially available powered
wheelchair (PWC) platform avoids a host of seating, mechanical design and
reliability issues but requires methods of predicting and controlling the
motion of a device never intended for robotics. Analog joystick inputs are
subject to black-box transformations which may produce intuitive and adaptable
motion control for human operators, but complicate robotic control approaches;
furthermore, installation of standard axle mounted odometers on a commercial
PWC is difficult. In this work, we present an integrated hardware and software
system for predicting the motion of a commercial PWC platform that does not
require any physical or electronic modification of the chair beyond plugging
into an industry standard auxiliary input port. This system uses an RGB-D
camera and an Arduino interface board to capture motion data, including visual
odometry and joystick signals, via ROS communication. Future motion is
predicted using an autoregressive sparse Gaussian process model. We evaluate
the proposed system on real-world short-term path prediction experiments.
Experimental results demonstrate the system's efficacy when compared to a
baseline neural network model.Comment: The paper has been accepted to the International Conference on
Robotics and Automation (ICRA2018
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