ARIS: Autonomous Real-Time Interactive Social Robot
References
- A. Rossi et al., “BRILLO: Personalised HRI with a Bartender Robot, ” Int. J. Soc. Robot., vol. 17, 2025; doi: 10.1007/s12369-025-01228-3.
- S. Ekstrom, L. Pareto, and Y.S. Ljungblad, “Teaching in a Collaborative Mathematic Learning Activity with and without a Social Robot, ” Educ. Inf. Technol., vol. 30, no. 1, 2025, pp. 1301–1328; doi: 10.1007/s10639-024-12926-2.
- S.S. Yeung, M. Ma, and T.T. Law, “Let’s listen and Tell a Story Together: Social Robot and Multidimensional Learning Engagement among Young Learners, ” AI Brain Child, vol. 1, no. 1, 2025, p. 5; doi: 10.1007/s44436-025-00006-2.
- R. Kuhne et al., “How Does Children’s Anthropomorphism of a Social Robot Develop Over Time? A Six-Wave Panel Study” Int. J. Soc. Robot., vol. 16, no. 7, 2024, pp. 1665–1679; doi: 10.1007/s12369-024-01155-9.
- S. Vinanzi and A. Cangelosi, “CASPER: Cognitive Architecture for Social Perception and Engagement in Robots, ” Int. J. Soc. Robot., 2024; doi: 10.1007/s12369-024-01116-2.
- M.A. Gonzalez-Santamarta et al., ‘A Hybrid Cognitive Architecture to Generate, Control, Plan, and Monitor Behaviors for Interactive Autonomous Robots, ” Int. J. Soc. Robot., 2024; doi: 10.1007/s12369-024-01192-4.
- M.A. Gonzalez-Santmarta et al., “MERLIN2: MachinEd Ros 2 pLanINg, ” Softw. Impacts, vol. 15, 2023, p. 100477; doi: 10.1016/j.simpa.2023.100477.
- M.A. Gonzalez-Santamarta et al., “MERLIN a Cognitive Architecture for Service Robots, ” Appl. Sci., vol. 10, no. 17, 2020, p. 5989; doi: 10.3390/app10175989.
- F. Dellaert and A.W. Stroupe, “Linear 2D Localization and Mapping for Single and Multiple Robot Scenarios, ” Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002, pp. 688–694; doi: 10.1109/ROBOT.2002.1013438.
- C. Gonzalez and M. Adams, “Curvature Scale Space LiDAR Odometry and Mapping (LOAM), ” J. Intell. Robot. Syst., vol. 110, no. 2, 2024, p. 67; doi: 10.1007/s10846-024-02096-1.
- R. Mur-Artal and J.D. Tardos, “ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras, ” IEEE Trans. Robot., vol. 33, no. 5, 2017, pp. 1255–1262; doi: 10.1109/TRO.2017.2705103.
- M. Labbe and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-based SLAM, ” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014, pp. 2661–2666; doi: 10.1109/IROS.2014.6942926.
- M. Filipenko and I. Afanasyev, “Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment, ” 2018 International Conference on Intelligent Systems (IS), 2018, pp. 400–407; doi: 10.1109/IS.2018.8710464.
- T. Pire et al., “S-PTAM: Stereo Parallel Tracking and Mapping, ” Robot. Auton. Syst., vol. 93, 2017, pp. 27–42; doi: 10.1016/j.robot.2017.03.019.
- J.-M. Valin et al., “Robust Recognition of Simultaneous Speech by a Mobile Robot, ” IEEE Trans. Robot., vol. 23, no. 4, 2007, pp. 742–752; doi: 10.1109/TRO.2007.900612.
- S. Alharbi et al., “Automatic Speech Recognition: Systematic Literature Review, ” IEEE Access, vol. 9, 2021, pp. 131858–131876; doi: 10.1109/ACCESS.2021.3112535.
- C.T. Ishi et al., ‘A Robust Speech Recognition System for Communication Robots in Noisy Environments, ” IEEE Trans. Robot., vol. 24, no. 3, 2008, pp. 759–763; doi: 10.1109/TRO.2008.919305.
- J. Davila-Chacon, J. Liu, and S. Wermter, “Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization, ” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 1, 2019, pp. 138–150; doi: 10.1109/TNNLS.2018.2830119.
- S. Girirajan and A. Pandian, “Offline Automatic Speech Recognition System Based on Bidirectional Gated Recurrent Unit (Bi-GRU) with Convolution Neural Network, ” J. Mob. Multimed., 2022; doi: 10.13052/jmm1550-4646.1869.
- I. Okunevich et al., “MobileCharger: An Autonomous Mobile Robot with Inverted Delta Actuator for Robust and Safe Robot Charging, ” 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2021, pp. 1–8; doi: 10.1109/ETFA45728.2021.9613366.
- G. Vasilyev et al., “Walking Algorithm for Robotis Op3 Humanoid Robot with Force Sensors, ” 2019 12th International Conference on Developments in eSystems Engineering (DeSE), 2019, pp. 20–23; doi: 10.1109/DeSE.2019.00014.
- A.D. Ames, “Human-Inspired Control of Bipedal Walking Robots, ” IEEE Trans. Autom. Control, vol. 59, no. 5, 2014, pp. 1115–1130; doi: 10.1109/TAC.2014.2299342.
- J. Lafaye, D. Gouaillier, and P.-B. Wieber, “Linear Model Predictive Control of the Locomotion of Pepper, a Humanoid Robot with Omnidirectional Wheels, ” 2014IEEE-RASInternational Conference on Humanoid Robots, 2014, pp. 336–341; doi: 10.1109/HUMANOIDS.2014.7041381.
- A.K. Pandey and R. Gelin, “A Mass-Produced Sociable Humanoid Robot: Pepper: The First Machine of Its Kind, ” IEEE Robot. Autom. Mag., vol. 25, no. 3, 2018, pp. 40–48; doi: 10.1109/MRA.2018.2833157.
- Z. Bauer et al., “Refining the Fusion of Pepper Robot and Estimated Depth Maps Method for Improved 3D Perception, ” IEEE Access, vol. 7, 2019, pp. 185076–185085; doi: 10.1109/ACCESS.2019.2960798.
- L. Cascone et al., “DTPAAL: Digital Twinning Pepper and Ambient Assisted Living, ” IEEE Trans. Ind. Inform., vol. 18, no. 2, 2022, pp. 1397–1404; doi: 10.1109/TII.2021.3090363.
- G. Ercolano et al., “Gesture Recognition with a 2D Low-Resolution Embedded Camera to Minimise Intrusion in Robot-Led Training of Children with Autism Spectrum Disorder, ” Appl. Intell., vol. 54, no. 8, 2024, pp. 6579–6591; doi: 10.1007/s10489-024-05477-z.
- Y. Wei, “A Comprehensive Approach to the Generation of Human-Like Arm Movements on Robot NAO, ” IEEE Access, vol. 8, 2020, pp. 172869–172881; doi: 10.1109/ACCESS.2020.3025532.
- C. Zheng, P. Zhang, and Y. Li, “Semantic SLAM System for Mobile Robots Based on Large Visual Model in Complex Environments, ” Sci. Rep., vol. 15, no. 1, 2025, p. 8450; doi: 10.1038/s41598-025-90340-5.
- K.M. Ahmad Yousef et al., “Personal Assistant Robot using Reinforcement Learning: DARWIN-OP2 as a Case Study, ” Intell. Serv. Robot., vol. 17, no. 4, 2024, pp. 815–831; doi: 10.1007/s11370-024-00540-7.
- P. Allgeuer et al., “The igus Humanoid Open Platform, ” KI - Künstl. Intell., vol. 30, no. 3, 2016, pp. 315–319; doi: 10.1007/s13218-016-0448-6.
- G. Ficht et al., “NimbRo-OP2X: Adult-Sized OpenSource 3D Printed Humanoid Robot, ”2018IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), 2018, pp. 1–9; doi: 10.1109/HUMANOIDS.2018.8625038.
- T.P. Tomo et al., “A New Silicone Structure for uSkin—A Soft, Distributed, Digital 3-Axis Skin Sensor and Its Integration on the Humanoid Robot iCub, ” IEEE Robot. Autom. Lett., vol. 3, no. 3, 2018, pp. 2584–2591; doi: 10.1109/LRA.2018.2812915.
- F. Naveros etal., “VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model, ” IEEE Trans. Cybern., vol. 50, no. 11, 2020, pp. 4744–4757; doi: 10.1109/TCYB.2019.2899246.
- G. Lombardi etal., “Humanoid Facial Expressions as a Tool to Study Human Behaviour, ” Sci. Rep., vol. 14, no. 1, 2024, p. 133; doi: 10.1038/s41598-023-45825-6.
- L. Fortunati et al., “Exploring the Perceptions of Cognitive and Affective Capabilities of Four, Real, Physical Robots with a Decreasing Degree of Morphological Human Likeness, ” Int. J. Soc. Robot., vol. 15, no. 3, 2023, pp. 547–561; doi: 10.1007/s12369-021-00827-0.
- D. Suresh, F. Dax, and D. Borrmann, “Advancing Robotics with an Interactive and Musical Humanoid Robot Based on the InMoov Project, ” 2024 22nd International Conference on Research and Education in Mechatronics (REM), 2024, pp. 237–243; doi: 10.1109/REM63063.2024.10735692.
- J. Ma et al., “A Multi-LIDAR SLAM Method Based on Hector-SLAM, ” 2024 China Automation Congress (CAC), 2024, pp. 345–350; doi: 10.1109/CAC63892.2024.10865468.
- S. Huang et al., ‘A Robust 2D Lidar SLAM Method in Complex Environment, ” Photonic Sens., vol. 12, no. 4, 2022, p. 220416; doi: 10.1007/s13320-022-0657-6.
- N. Abdelaziz and A. El-Rabbany, “LiDAR/Visual SLAM-Aided Vehicular Inertial Navigation System for GNSS-Denied Environments, ” 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICC-SPA), 2022, pp. 1–5; doi: 10.1109/ICCSPA55860.2022.10019210.
- Q. Du et al., “Tightly-coupled LidarVisual-Inertial SLAM for Mobile Robot, ” 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2024, pp. 842–845; doi: 10.1109/ICICML63543.2024.10957948.
- J. Lin et al., “R ∧2 LIVE: A Robust, Real-Time, LiDAR-Inertial-Visual Tightly-Coupled State Estimator and Mapping, ” IEEE Robot. Autom. Lett., vol. 6, no. 4, 2021, pp. 7469–7476; doi: 10.1109/LRA.2021.3095515.
- Z. Wang et al., “SLAM Mapping of Information Fusion between Lidar and Depth Camera, ” 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2022, pp. 142–145; doi: 10.1109/ICICML57342.2022.10009758.
- R.C. Hsu et al., “Real-Time Interaction System of Human-Robot with Hand Gestures, ” 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), 2020, pp. 396–398; doi: 10.1109/ECICE50847.2020.9301957.
- I. Diddeniya et al., “Human-Robot Communication System for an Isolated Environment, ” IEEE Access, vol. 10, 2022, pp. 63258–63269; doi: 10.1109/ACCESS.2022.3183110.
- D. Song et al., “VLM-Social-Nav: Socially Aware Robot Navigation Through Scoring Using VisionLanguage Models, ” IEEE Robot. Autom. Lett., vol. 10, no. 1, 2025, pp. 508–515; doi: 10.1109/LRA.2024.3511409.
Language: English
Page range: 9 - 19
Submitted on: Jun 21, 2025
Accepted on: Oct 29, 2025
Published on: Jun 22, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2026 Cesar Minaya-Andino, David Minango, Marcelo Zambrano, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.