7,534 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
Whisking with robots from rat vibrissae to biomimetic technology for active touch
This article summarizes some of the key features of the rat vibrissal system, including the actively controlled sweeping movements of the vibrissae known as whisking, and reviews the past and ongoing research aimed at replicating some of this functionality in biomimetic robots
Efficient Image-Space Extraction and Representation of 3D Surface Topography
Surface topography refers to the geometric micro-structure of a surface and
defines its tactile characteristics (typically in the sub-millimeter range).
High-resolution 3D scanning techniques developed recently enable the 3D
reconstruction of surfaces including their surface topography. In his paper, we
present an efficient image-space technique for the extraction of surface
topography from high-resolution 3D reconstructions. Additionally, we filter
noise and enhance topographic attributes to obtain an improved representation
for subsequent topography classification. Comprehensive experiments show that
the our representation captures well topographic attributes and significantly
improves classification performance compared to alternative 2D and 3D
representations.Comment: Initial version of the paper accepted at the IEEE ICIP Conference
201
Kiwi forego vison in the guidance of their nocturnal activities
We propose that the Kiwi visual system has undergone adaptive regression evolution driven by the trade-off between the relatively low rate of gain of visual information that is possible at low light levels, and the metabolic costs of extracting that information
The role of self-touch experience in the formation of the self
The human self has many facets: there is the physical body and then there are different concepts or representations supported by processes in the brain such as the ecological, social, temporal, conceptual, and experiential self. The mechanisms of operation and formation of the self are, however, largely unknown. The basis is constituted by the ecological or sensorimotor self that deals with the configuration of the body in space and its action possibilities. This self is prereflective, prelinguistic, and initially perhaps even largely independent of visual inputs. Instead, somatosensory (tactile and proprioceptive) information both before and after birth may play a key part. In this paper, we propose that self-touch experience may be a fundamental mechanisms to bootstrap the formation of the sensorimotor self and perhaps even beyond. We will investigate this from the perspectives of phenomenology, developmental psychology, and neuroscience. In light of the evidence from fetus and infant development, we will speculate about the possible mechanisms that may drive the formation of first body representations drawing on self-touch experience
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
This paper considers the problem of active object recognition using touch
only. The focus is on adaptively selecting a sequence of wrist poses that
achieves accurate recognition by enclosure grasps. It seeks to minimize the
number of touches and maximize recognition confidence. The actions are
formulated as wrist poses relative to each other, making the algorithm
independent of absolute workspace coordinates. The optimal sequence is
approximated by Monte Carlo tree search. We demonstrate results in a physics
engine and on a real robot. In the physics engine, most object instances were
recognized in at most 16 grasps. On a real robot, our method recognized objects
in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and
Systems (IROS) 201
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
This paper considers the problem of active object recognition using touch
only. The focus is on adaptively selecting a sequence of wrist poses that
achieves accurate recognition by enclosure grasps. It seeks to minimize the
number of touches and maximize recognition confidence. The actions are
formulated as wrist poses relative to each other, making the algorithm
independent of absolute workspace coordinates. The optimal sequence is
approximated by Monte Carlo tree search. We demonstrate results in a physics
engine and on a real robot. In the physics engine, most object instances were
recognized in at most 16 grasps. On a real robot, our method recognized objects
in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and
Systems (IROS) 201
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