The power of prediction: Robots that read intentions

Bicho, Estela; Erlhagen, Wolfram; Sousa, Emanuel; Louro, Luís; Hipólito, Nzoji; Silva, E. C.; Silva, R.; Ferreira, Flora; Machado, Toni; Hulstijn, Majken; Maas, Yvonne; Bruijn, Ellen de; Cuijpers, Raymond H.; Newman‐Norlund, Roger; Schie, Hein van; Meulenbroek, Ruud G. J.; Bekkering, Harold

doi:10.1109/iros.2012.6386297

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…Besides emotion and atmosphere, multi-robot behavior adaptation to intention is also a potential study base on the proposed intention understanding model. In some real application of HRI, intention understanding has been successfully applied in some autonomous robots, e.g., Pioneer 2DX robot [45] and ARoS [46], from which we infer the proposed TLFSVR based intention understanding model would be an effective way to autonomous robots for deeply understanding peoples' inner thoughts in HRI.…”

Section: Resultsmentioning

confidence: 98%

“…As the development of the cognitive science, in the real application of human-robot interaction, intention understanding has been successfully applied in some autonomous robots, e.g., Pioneer 2DX robot understands people's walking direction from experience using the Hidden Markov Models [45] and the ActivMedia Robotics Operating System (ARoS) robot as sociable partners in collaborative joint activity based on cognitively understanding of people's actions [46], showing the evidence that the proposed TLFSVR based intention understanding model may be an effective way to autonomous robots for deeply understanding peoples' inner thoughts in human-robot interaction.…”

Section: Application Experiments In Mrsmentioning

confidence: 99%

See 1 more Smart Citation

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Chen

Liu

et al. 2015

Int J of Soc Robotics

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An intention understanding model based on twolayer fuzzy support vector regression is proposed in humanrobot interaction, where fuzzy c-means clustering is used to classify the input data, and intention understanding is mainly obtained by emotion, with identification information such as age, gender, and nationality. It aims to realize the transparent communication by understanding customers' order intentions at a bar, in such a way that the social relationship between bar staffs and customers becomes smooth. To demonstrate the aptness of intention understanding model, experiments are designed in term of relationship between emotion-age-gender-nationality and order intention. Results show that the proposal obtains an intention understanding accuracy of 70 %/72 %/80 % with clusters number C = 2/3/6 (according to different genders/ages/nationalities), which is 23 %/26 %/33 % and 35.5 %/37.5 %/45.5 % higher than that of support vector regression (SVR) and back propagation neural networks (BPNN), respectively; the computational time of proposal is about 0.976 s/0.935 s/0.67 s with clusters number C = 2/3/6, while 1.889 s for SVR and 3.505 s for BPNN. Additionally, the preliminary application experiment is performed in the developing human-robot interaction system, called mascot robot system, where the experiment is performed in a scenario of "drinking at a bar", result shows that the bar lady robot obtains an accuracy of 77.8 % for understanding customers' order intentions and receives a satisfaction evaluation of "satisfied". According to the preliminary application, the proposal is being extended to an ordering system in the bar for business communication.

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Section: Resultsmentioning

confidence: 98%

Section: Application Experiments In Mrsmentioning

confidence: 99%

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Chen

Liu

et al. 2015

Int J of Soc Robotics

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“…It implements a highly context-sensitive mapping of an observed action of the co-worker onto an adequate complementary robot behavior. This mapping takes into account different task-related and user-related factors including an error monitoring capacity [6,8,54]. Neural populations encode in their suprathreshold activity a mismatch between which assembly step the operator should execute (shared task knowledge) and the predicted assembly step inferred from the observed motor behavior.…”

Section: Discussionmentioning

confidence: 99%

A neural integrator model for planning and value-based decision making of a robotics assistant

Wojtak

Ferreira

Vicente

et al. 2020

Neural Comput & Applic

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Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human-robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that applies brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over

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“…In neuroscience, the ideomotor principle (IMP) [114] emphasises the importance of anticipating (implicitly or explicitly) the sensory consequences of our actions and the actions of others for: 1) adaptive behaviour, 2) guidance of attention, 3) mentalising abilities and 4) social learning, all of which are a powerful means for building artificial cognitive systems that can acquire new knowledge autonomously, that learn from humans or that adapt to particular environments and preferences of the users [53,115].…”

Section: B Automatic Anticipatory and Predictive Mechanisms For Closmentioning

confidence: 99%

Attentional Mechanisms for Socially Interactive Robots–A Survey

Ferreira

Dias

2014

IEEE Trans. Auton. Mental Dev.

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This review intends to provide an overview of the state of the art in the modelling and implementation of automatic attentional mechanisms for socially interactive robots. Humans assess and exhibit intentionality by resorting to multisensory processes that are deeply rooted within low-level automatic attention-related mechanisms of the brain. For robots to engage with humans properly, they should also be equipped with similar capabilities. Joint attention, the precursor of many fundamental types of social interactions, has been an important focus of research in the past decade and a half, therefore providing the perfect backdrop for assessing the current status of stateof-the-art automatic attentional-based solutions. Consequently, we propose to review the influence of these mechanisms in the context of social interaction in cutting-edge research work on joint attention. This will be achieved by summarising the contributions already made in these matters in robotic cognitive systems research, by identifying the main scientific issues to be addressed by these contributions and analysing how successful they have been in this respect, and by consequently drawing conclusions that may suggest a roadmap for future successful research efforts.Index Terms-automatic attentional mechanisms, multisensory active perception, socially interactive robots, joint attention, bottom-up influences, top-down influences, probabilistic, hierarchical/modular architecture This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

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The power of prediction: Robots that read intentions

Cited by 10 publications

References 21 publications

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

A neural integrator model for planning and value-based decision making of a robotics assistant

Attentional Mechanisms for Socially Interactive Robots–A Survey

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