Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics
The integration of advanced robotics with sophisticated machine learning techniques offers substantial opportunities to enhance the autonomous capabilities of social robots. This paper presents a novel approach that enables a Pepper-type social robot to autonomously follow a person. Our methodology...
- Autores:
-
Ortiz Almanza, David Santiago
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74468
- Acceso en línea:
- https://hdl.handle.net/1992/74468
- Palabra clave:
- Person following
Convolutional neural networks
Reinforcement learning
Social robotics
Pepper robot
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial 4.0 International
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Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| title |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| spellingShingle |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics Person following Convolutional neural networks Reinforcement learning Social robotics Pepper robot Ingeniería |
| title_short |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| title_full |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| title_fullStr |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| title_full_unstemmed |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| title_sort |
Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics |
| dc.creator.fl_str_mv |
Ortiz Almanza, David Santiago |
| dc.contributor.advisor.none.fl_str_mv |
Lozano Martínez, Fernando Enrique |
| dc.contributor.author.none.fl_str_mv |
Ortiz Almanza, David Santiago |
| dc.contributor.jury.none.fl_str_mv |
Osma Cruz, Johann Faccelo |
| dc.subject.keyword.eng.fl_str_mv |
Person following Convolutional neural networks |
| topic |
Person following Convolutional neural networks Reinforcement learning Social robotics Pepper robot Ingeniería |
| dc.subject.keyword.none.fl_str_mv |
Reinforcement learning Social robotics Pepper robot |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
The integration of advanced robotics with sophisticated machine learning techniques offers substantial opportunities to enhance the autonomous capabilities of social robots. This paper presents a novel approach that enables a Pepper-type social robot to autonomously follow a person. Our methodology combines Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL) within a modular architecture. This architecture utilizes both 2D and 3D cameras as sensors, which provide robust real-time detection and tracking, while RL allows adaptive movement control. We have tested our implementation, demonstrating high performance and reliability across various complex environments. The results from these tests confirm that the proposed architecture and learning methods greatly enhance the robot’s ability to autonomously follow a person. This research represents a significant advancement in the practical deployment of autonomous systems in the field of social robotics. |
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2024 |
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2024-07-05T15:05:14Z |
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2024-07-04 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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eng |
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eng |
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Shay Aharon, Louis-Dupont, Ofri Masad, Kate Yurkova, Lotem Fridman, Lkdci, Eugene Khvedchenya, Ran Rubin, Natan Bagrov, Borys Tymchenko, Tomer Keren, Alexander Zhilko, and Eran-Deci. Super-gradients, 2021. URL https://zenodo.org/record/7789328. Redhwan Algabri and Mun-Taek Choi. Deep-learning-based indoor human following of mobile robot using color feature. Sensors, 20(9), 2020. ISSN 1424-8220. doi:10.3390/s20092699. URL https://www.mdpi.com/1424-8220/20/9/2699. Masashi Awai, Takahito Shimizu, Toru Kaneko, Atsushi Yamashita, and Hajime Asama. Hog-based person following and autonomous returning using generated map by mobile robot equipped with camera and laser range finder. In Sukhan Lee, Hyungsuck Cho, Kwang-Joon Yoon, and Jangmyung Lee, editors, Intelligent Autonomous Systems 12, pages 51–60, Berlin, Heidelberg, 2013. Springer Berlín Heidelberg. ISBN 978-3-642-33932-5. Maxime Busy and Maxime Caniot. qibullet, a bullet-based simulator for the pepper and nao robots. arXiv preprint arXiv:1909.00779, 2019. Bao Xin Chen, Raghavender Sahdev, and John K. Tsotsos. Integrating stereo visión with a cnn tracker for a person following robot. In Ming Liu, Haoyao Chen, and Markus Vincze, editors, Computer Vision Systems, pages 300–313, Cham, 2017. Springer International Publishing. Erwin Coumans and Yunfei Bai. Pybullet, a python module for physics simulation for games, robotics and machine learning. http://pybullet.org, 2016–2021. Peter Dayan and Yael Niv. Reinforcement learning: The good, the bad and the ugly. Current opinion in neurobiology, 18:185–96, 09 2008. doi: 10.1016/j.conb.2008.08.003. Maartje de Graaf, Somaya Allouch, and Jan A.G.M. Van Dijk. What makes robots social?: A user’s perspective on characteristics for social human-robot interaction. 10 2015. ISBN 978-3-319-25553-8. doi: 10.1007/978-3-319-25554-519. Yunhao Du, Zhicheng Zhao, Yang Song, Yanyun Zhao, Fei Su, Tao Gong, and Hongying Meng. Strongsort: Make deepsort great again, 2023. Beatriz Quintino Ferreira, Kelly Karipidou, Filipe Rosa, Sofia Petisca, Patrícia Alves-Oliveira, and Ana Paiva. A study on trust in a robotic suitcase. In Arvin Agah, John-John Cabibihan, Ayanna M. Howard, Miguel A. Salichs, and Hongsheng He, editors, Social Robotics, pages 179–189, Cham, 2016. Springer International Publishing. ISBN 978-3-319-47437-3. Juan José García Cárdenas. ópera aprende a jugar linea 4 con aprendizaje por refuerzo. Technical report, Universidad de los Andes, 2019. URL http://hdl.handle.net/1992/44471. César Daniel Garrido Urbano. Sistema de navegación para robot móvil basado en aprendizaje por refuerzo. Technical report, Universidad de los Andes, 2020. URL http://hdl.handle.net/1992/48862. Cesar Luis González Gutiérrez. Sistema de navegación autónoma para robot pepper basado en aprendizaje por refuerzo. Technical report, Universidad de los Andes, 2022. URL http://hdl.handle.net/1992/64472. Frank Hegel, Claudia Muhl, Britta Wrede, Martina Hielscher-Fastabend, and Gerhard Sagerer. Understanding social robots. pages 169–174, 02 2009. doi: 10.1109/ACHI.2009.51. Noriyuki Kawarazaki, Lucas Tetsuya Kuwae, and Tadashi Yoshidome. Development of human following mobile robot system using laser range scanner. Procedia Computer Science, 76:455–460, 2015. ISSN 1877-0509. doi: https://doi.org/10.1016/j.procs.2015.12.310. URL https://www.sciencedirect.com/science/article/pii/S1877050915038119. 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IEEE IRIS2015). Dat Nguyen Ngoc, Valerio Ponzi, Samuele Russo, and Francesco Vincelli. Supporting impaired people with a following robotic assistant by means of end-to-end visual target navigation and reinforcement learning approaches. In ICYRIME 2021 International Conference of Yearly Reports on Informatics Mathematics, and Engineering 2021, CEUR workshop proceedings, July 2021. OpenAI. Openai baselines: Proximal policy optimization. https://openai.com/index/openai-baselines-ppo/, 2018a. Accessed: 2024-06-01. OpenAI. Spinning up in deep reinforcement learning. https://spinningup.openai.com/en/latest/spinningup/rl intro2.html, 2018b. Accessed: 2024-06-01. Juan Esteban Padilla Torres. Detección de emociones utilizando aprendizaje de máquina multimodal para mejorar la interacción humano-robot de pepper. Technical report, Universidad de los Andes, 2023. URL http://hdl.handle.net/1992/68683. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection, 2016. Juan Andrés Romero Colmenares and Luccas Rojas Becerra. Improving autonomy and natural interaction with a pepper robot through the evaluation of different large language models. Technical report, Universidad de los Andes, 2023. URL https://hdl.handle.net/1992/73186. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov.Proximal policy optimization algorithms, 2017. Avinash Kumar Singh, Neha Baranwal, and Kai-Florian Richter. An empirical review of calibration techniques for the pepper humanoid robot’s rgb and depth camera. In Yaxin Bi, Rahul Bhatia, and Supriya Kapoor, editors, Intelligent Systems and Applications, pages 1026–1038, Cham, 2020. Springer International Publishing. ISBN 978-3-030-29513-4. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. URL http://incompleteideas.net/book/the-book-2nd.html. Hamid Taheri and Seyed Rasoul Hosseini. Deep reinforcement learning with enhanced ppo for safe mobile robot navigation, 2024. Juan Terven, Diana-Margarita Córdova-Esparza, and Julio-Alejandro Romero- González. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Machine Learning and Knowledge Extraction, 5(4): 1680–1716, November 2023. ISSN 2504-4990. doi: 10.3390/make5040083. URL http://dx.doi.org/10.3390/make5040083. Amari Tomoya, Satoru Nakayama, Atsushi Hoshina, and Midori Sugaya. A mobile robot for following, watching and detecting falls for elderly care. Procedia Computer Science, 112:1994–2003, 2017. ISSN 1877-0509. doi: https://doi.org/10.1016/j.procs.2017.08.125. URL https://www.sciencedirect.com/science/article/pii/S1877050917314825. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France. Ultralytics. Yolo nas: Modelos y arquitecturas. Ultralytics Documentation, 2023. URL https://docs.ultralytics.com/es/models/yolo-nas/. Libing Yang, Yang Li, and Long Chen. Clothppo: A proximal policy optimization enhancing framework for robotic cloth manipulation with observation-aligned action spaces, 2024. |
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Lozano Martínez, Fernando Enriquevirtual::18601-1Ortiz Almanza, David SantiagoOsma Cruz, Johann Faccelovirtual::18612-12024-07-05T15:05:14Z2024-07-05T15:05:14Z2024-07-04https://hdl.handle.net/1992/74468instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The integration of advanced robotics with sophisticated machine learning techniques offers substantial opportunities to enhance the autonomous capabilities of social robots. This paper presents a novel approach that enables a Pepper-type social robot to autonomously follow a person. Our methodology combines Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL) within a modular architecture. This architecture utilizes both 2D and 3D cameras as sensors, which provide robust real-time detection and tracking, while RL allows adaptive movement control. We have tested our implementation, demonstrating high performance and reliability across various complex environments. The results from these tests confirm that the proposed architecture and learning methods greatly enhance the robot’s ability to autonomously follow a person. This research represents a significant advancement in the practical deployment of autonomous systems in the field of social robotics.Pregrado30 páginasapplication/pdfengUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social roboticsTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPPerson followingConvolutional neural networksReinforcement learningSocial roboticsPepper robotIngenieríaShay Aharon, Louis-Dupont, Ofri Masad, Kate Yurkova, Lotem Fridman, Lkdci, Eugene Khvedchenya, Ran Rubin, Natan Bagrov, Borys Tymchenko, Tomer Keren, Alexander Zhilko, and Eran-Deci. Super-gradients, 2021. URL https://zenodo.org/record/7789328.Redhwan Algabri and Mun-Taek Choi. Deep-learning-based indoor human following of mobile robot using color feature. Sensors, 20(9), 2020. ISSN 1424-8220. doi:10.3390/s20092699. URL https://www.mdpi.com/1424-8220/20/9/2699.Masashi Awai, Takahito Shimizu, Toru Kaneko, Atsushi Yamashita, and Hajime Asama. Hog-based person following and autonomous returning using generated map by mobile robot equipped with camera and laser range finder. In Sukhan Lee, Hyungsuck Cho, Kwang-Joon Yoon, and Jangmyung Lee, editors, Intelligent Autonomous Systems 12, pages 51–60, Berlin, Heidelberg, 2013. Springer Berlín Heidelberg. ISBN 978-3-642-33932-5.Maxime Busy and Maxime Caniot. qibullet, a bullet-based simulator for the pepper and nao robots. arXiv preprint arXiv:1909.00779, 2019.Bao Xin Chen, Raghavender Sahdev, and John K. Tsotsos. Integrating stereo visión with a cnn tracker for a person following robot. In Ming Liu, Haoyao Chen, and Markus Vincze, editors, Computer Vision Systems, pages 300–313, Cham, 2017. Springer International Publishing.Erwin Coumans and Yunfei Bai. Pybullet, a python module for physics simulation for games, robotics and machine learning. http://pybullet.org, 2016–2021.Peter Dayan and Yael Niv. Reinforcement learning: The good, the bad and the ugly. Current opinion in neurobiology, 18:185–96, 09 2008. doi: 10.1016/j.conb.2008.08.003.Maartje de Graaf, Somaya Allouch, and Jan A.G.M. Van Dijk. What makes robots social?: A user’s perspective on characteristics for social human-robot interaction. 10 2015. ISBN 978-3-319-25553-8. doi: 10.1007/978-3-319-25554-519.Yunhao Du, Zhicheng Zhao, Yang Song, Yanyun Zhao, Fei Su, Tao Gong, and Hongying Meng. Strongsort: Make deepsort great again, 2023.Beatriz Quintino Ferreira, Kelly Karipidou, Filipe Rosa, Sofia Petisca, Patrícia Alves-Oliveira, and Ana Paiva. A study on trust in a robotic suitcase. In Arvin Agah, John-John Cabibihan, Ayanna M. Howard, Miguel A. Salichs, and Hongsheng He, editors, Social Robotics, pages 179–189, Cham, 2016. Springer International Publishing. ISBN 978-3-319-47437-3.Juan José García Cárdenas. ópera aprende a jugar linea 4 con aprendizaje por refuerzo. Technical report, Universidad de los Andes, 2019. URL http://hdl.handle.net/1992/44471.César Daniel Garrido Urbano. Sistema de navegación para robot móvil basado en aprendizaje por refuerzo. Technical report, Universidad de los Andes, 2020. URL http://hdl.handle.net/1992/48862.Cesar Luis González Gutiérrez. Sistema de navegación autónoma para robot pepper basado en aprendizaje por refuerzo. Technical report, Universidad de los Andes, 2022. URL http://hdl.handle.net/1992/64472.Frank Hegel, Claudia Muhl, Britta Wrede, Martina Hielscher-Fastabend, and Gerhard Sagerer. Understanding social robots. pages 169–174, 02 2009. doi: 10.1109/ACHI.2009.51.Noriyuki Kawarazaki, Lucas Tetsuya Kuwae, and Tadashi Yoshidome. Development of human following mobile robot system using laser range scanner. Procedia Computer Science, 76:455–460, 2015. ISSN 1877-0509. doi: https://doi.org/10.1016/j.procs.2015.12.310. URL https://www.sciencedirect.com/science/article/pii/S1877050915038119. 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IEEE IRIS2015).Dat Nguyen Ngoc, Valerio Ponzi, Samuele Russo, and Francesco Vincelli. Supporting impaired people with a following robotic assistant by means of end-to-end visual target navigation and reinforcement learning approaches. In ICYRIME 2021 International Conference of Yearly Reports on Informatics Mathematics, and Engineering 2021, CEUR workshop proceedings, July 2021.OpenAI. Openai baselines: Proximal policy optimization. https://openai.com/index/openai-baselines-ppo/, 2018a. Accessed: 2024-06-01.OpenAI. Spinning up in deep reinforcement learning. https://spinningup.openai.com/en/latest/spinningup/rl intro2.html, 2018b. Accessed: 2024-06-01.Juan Esteban Padilla Torres. Detección de emociones utilizando aprendizaje de máquina multimodal para mejorar la interacción humano-robot de pepper. Technical report, Universidad de los Andes, 2023. URL http://hdl.handle.net/1992/68683.Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection, 2016.Juan Andrés Romero Colmenares and Luccas Rojas Becerra. Improving autonomy and natural interaction with a pepper robot through the evaluation of different large language models. Technical report, Universidad de los Andes, 2023. URL https://hdl.handle.net/1992/73186.John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov.Proximal policy optimization algorithms, 2017.Avinash Kumar Singh, Neha Baranwal, and Kai-Florian Richter. An empirical review of calibration techniques for the pepper humanoid robot’s rgb and depth camera. In Yaxin Bi, Rahul Bhatia, and Supriya Kapoor, editors, Intelligent Systems and Applications, pages 1026–1038, Cham, 2020. Springer International Publishing. ISBN 978-3-030-29513-4.Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. URL http://incompleteideas.net/book/the-book-2nd.html.Hamid Taheri and Seyed Rasoul Hosseini. Deep reinforcement learning with enhanced ppo for safe mobile robot navigation, 2024.Juan Terven, Diana-Margarita Córdova-Esparza, and Julio-Alejandro Romero- González. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Machine Learning and Knowledge Extraction, 5(4): 1680–1716, November 2023. ISSN 2504-4990. doi: 10.3390/make5040083. URL http://dx.doi.org/10.3390/make5040083.Amari Tomoya, Satoru Nakayama, Atsushi Hoshina, and Midori Sugaya. A mobile robot for following, watching and detecting falls for elderly care. Procedia Computer Science, 112:1994–2003, 2017. ISSN 1877-0509. doi: https://doi.org/10.1016/j.procs.2017.08.125. URL https://www.sciencedirect.com/science/article/pii/S1877050917314825. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France.Ultralytics. Yolo nas: Modelos y arquitecturas. Ultralytics Documentation, 2023. URL https://docs.ultralytics.com/es/models/yolo-nas/.Libing Yang, Yang Li, and Long Chen. Clothppo: A proximal policy optimization enhancing framework for robotic cloth manipulation with observation-aligned action spaces, 2024.201913600Publicationhttps://scholar.google.es/citations?user=6QQ-dqMAAAAJvirtual::18612-1https://scholar.google.es/citations?user=6QQ-dqMAAAAJ0000-0003-2928-3406virtual::18612-10000-0003-2928-3406https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000025550virtual::18601-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000221112virtual::18612-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000221112edd81d8c-e0b9-4c1f-bf04-eed0e12e755dvirtual::18601-1edd81d8c-e0b9-4c1f-bf04-eed0e12e755dvirtual::18601-1a9f6ef37-65d7-4484-be71-8f3b4067a8favirtual::18612-1a9f6ef37-65d7-4484-be71-8f3b4067a8faa9f6ef37-65d7-4484-be71-8f3b4067a8favirtual::18612-1ORIGINALIntegrating convolutional neural networks and reinforcement learning for autonomous person-following in 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