82 research outputs found
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
This paper presents a computational model of the processing of dynamic
spatial relations occurring in an embodied robotic interaction setup. A
complete system is introduced that allows autonomous robots to produce and
interpret dynamic spatial phrases (in English) given an environment of moving
objects. The model unites two separate research strands: computational
cognitive semantics and on commonsense spatial representation and reasoning.
The model for the first time demonstrates an integration of these different
strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in
Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science,
pages 958-971. Springe
A Practical Guide to Studying Emergent Communication through Grounded Language Games
The question of how an effective and efficient communication system can
emerge in a population of agents that need to solve a particular task attracts
more and more attention from researchers in many fields, including artificial
intelligence, linguistics and statistical physics. A common methodology for
studying this question consists of carrying out multi-agent experiments in
which a population of agents takes part in a series of scripted and
task-oriented communicative interactions, called 'language games'. While each
individual language game is typically played by two agents in the population, a
large series of games allows the population to converge on a shared
communication system. Setting up an experiment in which a rich system for
communicating about the real world emerges is a major enterprise, as it
requires a variety of software components for running multi-agent experiments,
for interacting with sensors and actuators, for conceptualising and
interpreting semantic structures, and for mapping between these semantic
structures and linguistic utterances. The aim of this paper is twofold. On the
one hand, it introduces a high-level robot interface that extends the Babel
software system, presenting for the first time a toolkit that provides flexible
modules for dealing with each subtask involved in running advanced grounded
language game experiments. On the other hand, it provides a practical guide to
using the toolkit for implementing such experiments, taking a grounded colour
naming game experiment as a didactic example.Comment: This paper was officially published at the 'Language Learning for
Artificial Agents (L2A2) Symposium' of the 2019 Artificial Intelligence and
Simulation of Behaviour (AISB) Conventio
A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
Picking up objects requested by a human user is a common task in human-robot
interaction. When multiple objects match the user's verbal description, the
robot needs to clarify which object the user is referring to before executing
the action. Previous research has focused on perceiving user's multimodal
behaviour to complement verbal commands or minimising the number of follow up
questions to reduce task time. In this paper, we propose a system for reference
disambiguation based on visualisation and compare three methods to disambiguate
natural language instructions. In a controlled experiment with a YuMi robot, we
investigated real-time augmentations of the workspace in three conditions --
mixed reality, augmented reality, and a monitor as the baseline -- using
objective measures such as time and accuracy, and subjective measures like
engagement, immersion, and display interference. Significant differences were
found in accuracy and engagement between the conditions, but no differences
were found in task time. Despite the higher error rates in the mixed reality
condition, participants found that modality more engaging than the other two,
but overall showed preference for the augmented reality condition over the
monitor and mixed reality conditions
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
Synthetic modeling of cultural language evolution
Trabajo presentado al EvolangIX Workshop: "Five Approaches to Language Evolution" celebrado en Kyoto (Japón) el 13 de marzo de 2012.Recently cultural theories of language evolution have gained significant momentum in explaining
natural language. This paper reviews agent-based modeling, one of the key methodologies which is in part responsible for these developments. We discuss the most important challenges for a theory of cultural language evolution and the resulting dominant experimental paradigm. The discussion is framed along examples of experiments conducted within the methodology. We focus, in particular, on spatial language as an example of a complex and cognitively central domain treated in a series of robotic experiments.Funding was provided by Sony CSL Paris, the EU FP6 project ECAgents and the EU FP7 project Alear.Peer reviewe
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