17,063 research outputs found
Robot Navigation in Unseen Spaces using an Abstract Map
Human navigation in built environments depends on symbolic spatial
information which has unrealised potential to enhance robot navigation
capabilities. Information sources such as labels, signs, maps, planners, spoken
directions, and navigational gestures communicate a wealth of spatial
information to the navigators of built environments; a wealth of information
that robots typically ignore. We present a robot navigation system that uses
the same symbolic spatial information employed by humans to purposefully
navigate in unseen built environments with a level of performance comparable to
humans. The navigation system uses a novel data structure called the abstract
map to imagine malleable spatial models for unseen spaces from spatial symbols.
Sensorimotor perceptions from a robot are then employed to provide purposeful
navigation to symbolic goal locations in the unseen environment. We show how a
dynamic system can be used to create malleable spatial models for the abstract
map, and provide an open source implementation to encourage future work in the
area of symbolic navigation. Symbolic navigation performance of humans and a
robot is evaluated in a real-world built environment. The paper concludes with
a qualitative analysis of human navigation strategies, providing further
insights into how the symbolic navigation capabilities of robots in unseen
built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and
Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see
https://btalb.github.io/abstract_map/ for access to softwar
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ïŹndings
in cognitive psychology, our model is composed of layers representing maps at diïŹerent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the ïżœexperimenterïżœ, and Mary, the ïżœcomputational modellerïżœ. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
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