Lingodroids: Learning terms for time

Heath, Scott; Schulz, Ruth; Ball, David; Wiles, Janet

doi:10.1109/icra.2012.6225130

Cited by 6 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The XS Condition uses XSL to resolve the referential uncertainty between space and time. In the Innate Condition links between word and concept are known a priori (identical to previous studies Schulz et al (2011a) and Heath et al (2012a)). …”

Section: Study Conditions: Innate Vs Cross-situational Learningmentioning

confidence: 99%

“…Several studies have looked at learning grounded terms for space and time, in our own group and others, to name spatial prepositions (Steels, 1995(Steels, , 1999Roy, 2002a;Cangelosi et al, 2005), route descriptions (Levit and Roy, 2007;Tellex et al, 2011), spatial relations (Spranger et al, 2014), toponyms (Jung and Zelinsky, 2000;Schulz et al, 2011a), landmarks (Spranger, 2012(Spranger, , 2013, durations (Schulz et al, 2011b;Heath et al, 2012a) and event-based time (Steels and Baillie, 2003;Heath et al, 2012b). These studies can be divided into those that ground directly in perception (Steels, 1995(Steels, , 1999Roy, 2002a;Spranger et al, 2014) and those that ground in higher-level representations of space and time (Jung and Zelinsky, 2000;Steels and Baillie, 2003;Cangelosi et al, 2005;Levit and Roy, 2007;Schulz et al, 2011a;Tellex et al, 2011;Heath et al, 2012aHeath et al, , 2012b. The latter group are more relevant to these studies since they require intermediate representations between perception and symbols to allow agents to ground symbols in abstractions that go beyond what is directly perceptible (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…The Lingodroid temporal lexicons from the how-long-since-we-met studies were tested using a series of meet-at tasks in which one robot specified a location and future time, and both robots attempted to meet at the specified location and time (Heath et al, 2012a). Despite high coherence between the robots' lexicons, meet-at proved a challenging task.…”

Section: Robots Clock Time and Grounded Languagementioning

confidence: 99%

“…The agents in these studies use their internal clocks for counting time, and use the event of their last meeting as the beginning of a duration (as per Heath et al (2012a)). This allows the agents to ground temporal words that correspond to the durations associated with "a short time", "a long time", "a little while".…”

Section: Temporal Cognitionmentioning

confidence: 99%

“…The Previous work in the Lingodroids project has shown that real robots can learn a language for human-like concepts of space (locations and spatial relationships (Schulz et al, 2011a)), and that this framework can be extended to temporal concepts of durations (Heath et al, 2012a). In previous Lingodroid studies, the robots constructed their maps independently, and so have had unique cognitive maps of the world, but within each study the robots were functionally identical, using the same type of physical robot and the same underlying mapping algorithms and behaviors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evolving spatial and temporal lexicons across different cognitive architectures

Heath¹

Self Cite

View full text Add to dashboard Cite

Communication between mobile robots requires a transfer of symbols, where each symbol signifies a meaning. However, in typical applications, meaning has been ascribed to the symbols by the engineers that have programmed the robots. This thesis explores an alternative: the use of algorithms and representations that allow mobile robots to evolve a shared set of symbols where the meanings of the symbols are derived from the robots' sensors and cognition.Mobile robots have two important properties that affect the learning of symbols, i) that they are capable of locomotion through space over time; and ii) that they come in many different configurations with different architectures. Previous work has demonstrated that mobile robots can learn shared lexicons to describe space through perceptual referents and referents grounded in cognitive maps. However, open questions remain as to how mobile robots can learn to communicate using temporal terms, and how learning lexicons is affected by different cognitive architectures.The major research question addressed in this thesis is how can mobile robots develop spatial and temporal lexicons across different cognitive architectures? Three facets of language learning are considered particularly important for robots with different cognitive architectures: i) the ability to ground terms in cognition;ii) the ability to ground identical terms in different sensors and cognition for each robot; and iii) the ability to handle referential uncertainty -the difficulty of linking words to meanings within ambiguous contexts.Pairs of mobile robots are used to develop lexicons for spatial and temporal terms and to study each of these abilities. The terms developed by the robots are tested by organizing spatial and temporal tasks and extended to additional terms through grounding transfer.In this thesis, language learning is studied within a framework defined by Peirce's semiotic triangle and building on previous Lingodroid studies. Conversations between robots are used to socially ground symbols within the robots' spatial and temporal cognition. Distributed lexicon tables are used to store links between words and meanings. As the lexicons evolve the words are analyzed for immediate usability, and the final lexicons are analyzed for coherence.Four studies to analyze different aspects of lexicon learning were completed. Study I addressed the aims of learning duration terms using mobile robots and using grounded spatial and temporal language together to perform joint tasks. Identical mobile robots were used to ground terms for time in durations using clocks (time since the last meeting). The robots were able to develop coherent lexicons, and successfully organize future meetings using learned terms.Study II addressed the aim of learning event-based temporal terms using mobile robots. Identical mobile robots were used to ground terms for time in sunlight levels (time of day). The robots required the ability to ground terms in features formed from a brightness level and its derivative. Aga...

show abstract

Section: Study Conditions: Innate Vs Cross-situational Learningmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Robots Clock Time and Grounded Languagementioning

confidence: 99%

Section: Temporal Cognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evolving spatial and temporal lexicons across different cognitive architectures

Heath¹

Self Cite

View full text Add to dashboard Cite

show abstract

Lingodroids: Investigating grounded color relations using a social robot for children

Heath¹,

Hensby²,

Bodén³

et al. 2016

2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

View full text Add to dashboard Cite

Communication between Lingodroids with different cognitive capabilities

Heath

Ball

Schulz

et al. 2013

2013 IEEE International Conference on Robotics and Automation

Self Cite

View full text Add to dashboard Cite

Previous studies have shown how Lingodroids, language learning mobile robots, learn terms for space and time, connecting their personal maps of the world to a publically shared language. One caveat of previous studies was that the robots shared the same cognitive architecture, identical in all respects from sensors to mapping systems. In this paper we investigate the question of how terms for space can be developed between robots that have fundamentally different sensors and spatial representations. In the real world, communication needs to occur between agents that have different embodiment and cognitive capabilities, including different sensors, different representations of the world, and different species (including humans). The novel aspects of these studies is that one robot uses a forward facing camera to estimate appearance and uses a biologically inspired continuous attractor network to generate a topological map; the other robot uses a laser scanner to estimate range and uses a probabilistic filter approach to generate an occupancy grid. The robots hold conversations in different locations to establish a shared language. Despite their different ways of sensing and mapping the world, the robots are able to create coherent lexicons for the space around them.

show abstract

Lingodroids: Learning terms for time

Cited by 6 publications

References 14 publications

Evolving spatial and temporal lexicons across different cognitive architectures

Evolving spatial and temporal lexicons across different cognitive architectures

Lingodroids: Investigating grounded color relations using a social robot for children

Communication between Lingodroids with different cognitive capabilities

Contact Info

Product

Resources

About