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A neurorobotics approach to behaviour selection based on human activity recognition

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Abstract

Behaviour selection has been an active research topic for robotics, in particular in the field of human–robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia–thalamus–cortex (BG–T–C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.

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Data availability

The HWU-USP activities dataset is available at Data Dryad (Ranieri et al. 2021). All code employed in this paper is available at Github, under the following repositories: Activity recognition framework: https://github.com/cmranieri/Deep-Activity-Recognition, Bioinspired computational model and decoder: https://github.com/cmranieri/Bioinspired-behaviour, Robot simulation: https://github.com/cmranieri/robot-simulation

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Funding

This work was funded by the Sao Paulo Research Foundation (FAPESP), grants 2017/02377-5, 2017/01687-0, 2018/25902-0 and 2021/10921-2, and the Neuro4PD project - Royal Society and Newton Fund (NAF/ R2/180773). Moioli acknowledge the support from the Brazilian institutions: INCT INCEMAQ of the CNPq/MCTI, FAPERN, CAPES, FINEP, and MEC. This research was carried out using the computational resources from the CeMEAI funded by FAPESP, grant 2013/07375-0. Additional resources were provided by the Robotics Lab within the ECR, and by the Nvidia Grants program.

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Authors and Affiliations

  1. Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP, 13566-590, Brazil

    Caetano M. Ranieri & Roseli A. F. Romero

  2. Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Avenida Senador Salgado Filho, 3000, Natal, RN, 59078-970, Brazil

    Renan C. Moioli

  3. Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK

    Patricia A. Vargas

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  1. Caetano M. Ranieri
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  2. Renan C. Moioli
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  3. Patricia A. Vargas
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  4. Roseli A. F. Romero
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Contributions

All authors contributed to the design of the experiments. Ranieri provided the specific methods and implementations, performed the experiments and analysed the results. Moioli, Vargas and Romero revised the methods and results presented, contributing to the discussion. Ranieri written the draft of the paper, revised by the other authors.

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Correspondence to Caetano M. Ranieri.

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Ranieri, C.M., Moioli, R.C., Vargas, P.A. et al. A neurorobotics approach to behaviour selection based on human activity recognition. Cogn Neurodyn 17, 1009–1028 (2023). https://doi.org/10.1007/s11571-022-09886-z

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