1,077 research outputs found
Approaching the Symbol Grounding Problem with Probabilistic Graphical Models
In order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as āDrive over to receiving and pick up the tire pallet.ā In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our speciļ¬c approach is to develop models that factor according to the linguistic structure of a command. We ļ¬rst describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.U.S. Army Research Laboratory. Collaborative Technology Alliance Program (Cooperative Agreement W911NF-10-2-0016)United States. Office of Naval Research (MURI N00014-07-1-0749
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
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
Description Logic for Scene Understanding at the Example of Urban Road Intersections
Understanding a natural scene on the basis of external sensors is a task yet to be solved by computer algorithms. The present thesis investigates the suitability of a particular family of explicit, formal representation and reasoning formalisms for this task, which are subsumed under the term Description Logic
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