Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Sustainable cloud services for verbal interaction with embodied agents

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

This article presents the design and the implementation of a cloud system for knowledge-based autonomous interaction devised for Social Robots and other conversational agents. The system is particularly convenient for low-cost robots and devices: it can be used as a stand-alone dialogue system or as an integration to provide “background” dialogue capabilities to any preexisting natural language understanding ability that the robot may already have as part of its basic skills. By connecting to the cloud, developers are provided with a sustainable solution to manage verbal interaction through a network connection, with about 3000 topics of conversation ready for “chit-chatting” and a library of pre-cooked plans that only needs to be grounded into the robot’s physical capabilities. The system is structured as a set of REST API endpoints so that it can be easily expanded by adding new APIs to improve the capabilities of the clients connected to the cloud. Another key feature of the system is that it has been designed to make the development of its clients straightforward: in this way, multiple robots and devices can be easily endowed with the capability of autonomously interacting with the user, understanding when to perform specific actions, and exploiting all the information provided by cloud services. The article outlines and discusses the results of the experiments performed to assess the system’s performance in terms of response time, paving the way for its use both for research and market solutions. Links to repositories with clients for ROS and popular robots such as Pepper and NAO are provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Algorithm 1
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Data and materials are available on request.

Code availablity

The code for all the developed clients is available in the provided repositories.

Notes

  1. For instance: https://www.marketsandmarkets.com/Market-Reports/educational-robot-market-28174634.html and https://www.marketsandmarkets.com/Market-Reports/service-robotics-market-681.html.

  2. https://dialogflow.cloud.google.com.

  3. https://www.ibm.com/cloud/watson-assistant.

  4. https://azure.microsoft.com/en-us/services/bot-services/.

  5. https://aws.amazon.com/lex/.

  6. https://openai.com/blog/openai-api/.

  7. https://huggingface.co/EleutherAI/gpt-j-6B.

  8. https://huggingface.co/bigscience/bloom.

  9. https://flask-restful.readthedocs.io/en/latest/.

  10. https://github.com/lucregrassi/CAIRclient_Python3.git.

  11. https://github.com/lucregrassi/CAIRclient_SoftBank.git.

  12. https://github.com/lucregrassi/CAIRclient_ROS2.git.

  13. https://youtu.be/hgsFGDvvIww.

  14. https://youtu.be/27YvsH7IoSA.

  15. https://youtu.be/m22yRiR6JpQ.

  16. https://www.speedtest.net.

References

  1. Wan J, Tang S, Yan H, Li D, Wang S, Vasilakos AV (2016) Cloud robotics: current status and open issues. IEEE Access 4:2797–2807

    Google Scholar 

  2. Wan LC, Chan EK, Luo X (2020) Robots come to rescue: How to reduce perceived risk of infectious disease in covid19-stricken consumers? Ann Tour Res

  3. Abdi J, Al-Hindawi A, Ng T, Vizcaychipi MP (2018) Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open 8(2)

  4. Pu L, Moyle W, Jones C, Todorovic M (2019) The effectiveness of social robots for older adults: a systematic review and meta-analysis of randomized controlled studies. Gerontology 59(1):E37–E51

    Google Scholar 

  5. Papadopoulos C, Hill T, Battistuzzi L, Castro N, Nigath A, Randhawa G, Merton L, Kanoria S, Kamide H, Chong NY, Hewson D, Davidson R, Sgorbissa A (2020) The caresses study protocol: testing and evaluating culturally competent socially assistive robots among older adults residing in long term care homes through a controlled experimental trial. Arch Public Health 72(1)

  6. Papadopoulos C, Castro N, Nigath A, Davidson R, Faulkes N, Menicatti R, Khaliq AA, Recchiuto C, Battistuzzi L, Randhawa G, Merton L, Kanoria S, Chong N-Y, Kamide H, Hewson D, Sgorbissa A (2022) The caresses randomised controlled trial: exploring the health-related impact of culturally competent artificial intelligence embedded into socially assistive robots and tested in older adult care homes. Int J Soc Robot 14(1):245–256

    Article  Google Scholar 

  7. Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the NAACL HLT 2019, vol 1, pp 4171–4186

  8. Grassi L, Canepa D, Bellitto A, Casadio M, Massone A, Recchiuto CT, Sgorbissa A (2023) Diversity-aware verbal interaction between a robot and people with spinal cord injury. In: Proceedings of the IEEE RO-MAN 2023, Busan, South Korea, Accepted for publication

  9. Grassi L, Recchiuto CT, Sgorbissa A (2023) Robot-induced group conversation dynamics: a model to balance participation and unify communities. In: Proceedings of the IEEE/RSJ IROS 2023, Detroit (Submitted)

  10. Demutti M, D’Amato V, Recchiuto CT, Oneto L, Sgorbissa A (2022) Assessing emotions in human-robot interaction based on the appraisal theory. In: RO-MAN 2022, pp 1435–1442

  11. D’Angelo I, Morocutti L, Giunchiglia E, Recchiuto CT, Sgorbissa A (2023) Nice and nasty theory of mind for social and antisocial robots. In: RO-MAN 2023, Accepted for publication

  12. W3C (2012) Owl 2 web ontology language document overview (second edition). https://www.w3.org/TR/owl2-overview/. Accessed 2023-06-11

  13. Recchiuto CT, Sgorbissa A (2020) A feasibility study of culture-aware cloud services for conversational robots. IEEE Robot Autom Lett 5(4):6559–6566

    Article  Google Scholar 

  14. Bruno B, Recchiuto CT, Papadopoulos I, Saffiotti A, Koulouglioti C, Menicatti R, Mastrogiovanni F, Zaccaria R, Sgorbissa A (2019) Knowledge representation for culturally competent personal robots: requirements, design principles, implementation, and assessment. Int J Soc Robot 11(3):515–538

    Article  Google Scholar 

  15. Grassi L, Recchiuto CT, Sgorbissa A (2022) Knowledge-grounded dialogue flow management for social robots and conversational agents. Int J Soc Robot 14(5):1273–1293

    Article  Google Scholar 

  16. Kehoe B, Patil S, Abbeel P, Goldberg K (2015) A survey of research on cloud robotics and automation. Trans Autom 12(2):398–409

    Google Scholar 

  17. Aha O, Dasgupta P (2018) A comprehensive survey of recent trends in cloud robotics architectures and applications. Robotics 7(3)

  18. Afrin M, Jin J, Rahman A, Rahman A, Wan J, Hossain E (2021) Resource allocation and service provisioning in multi-agent cloud robotics: a comprehensive survey. IEEE Commun Surv Tutor 23(2):842–870

    Article  Google Scholar 

  19. Pignaton de Freitas E, Olszewska JI, Carbonera JL, Fiorini SR, Khamis A, Ragavan SV, Barreto ME, Prestes E, Habib MK, Redfield S, Chibani A, Goncalves P, Bermejo-Alonso J, Sanz R, Tosello E, Olivares-Alarcos A, Konzen AA, Quintas J, Li H (2023) Ontological concepts for information sharing in cloud robotics. J Amb Intell Humaniz Comput 14(5):4921–4932

    Article  Google Scholar 

  20. Mohanarajah G, Usenko V, Singh M, D’Andrea R, Waibel M (2015) Cloud-based collaborative 3d mapping in real-time with low-cost robots. IEEE Trans Autom Sci Eng 12(2):423–431

    Article  Google Scholar 

  21. Liu J, Xu W, Zhang J, Zhou Z, Pham DT (2016) Industrial cloud robotics towards sustainable manufacturing. In: Proceedings of the ASME MSEC 2016, vol 2, Blacksburg

  22. Cardarelli E, Digani V, Sabattini L, Secchi C, Fantuzzi C (2017) Cooperative cloud robotics architecture for the coordination of multi-agv systems in industrial warehouses. Mechatronics 45:1–13

    Article  Google Scholar 

  23. Yan H, Hua Q, Wang Y, Wei W, Imran M (2017) Cloud robotics in smart manufacturing environments: challenges and countermeasures. Comput Electr Eng 63:56–65

    Article  Google Scholar 

  24. Jiafu W, Shenglong T, Qingsong H, Di L, Chengliang L, Jaime L (2018) Context-aware cloud robotics for material handling in cognitive industrial internet of things. IEEE Internet Things J 5(4):2272–2281

    Article  Google Scholar 

  25. Waymo-formerly the Google self-driving car project (2016) https://waymo.com. Accessed 2023-06-11

  26. Chen W, Yaguchi Y, Naruse K, Watanobe Y, Nakamura K, Ogawa J (2018) A study of robotic cooperation in cloud robotics: architecture and challenges. IEEE Access 6:36662–36682

    Article  Google Scholar 

  27. Rahman A, Jin J, Cricenti AL, Rahman A, Kulkarni A (2019) Communication-aware cloud robotic task offloading with on-demand mobility for smart factory maintenance. IEEE Trans Ind Inform 15(5):2500–2511

    Article  Google Scholar 

  28. Liu B, Wang L, Liu M, Xu C-Z (2020) Federated imitation learning: a novel framework for cloud robotic systems with heterogeneous sensor data. IEEE Robot Autom Lett 5(2):3509–3516

    Article  Google Scholar 

  29. Chinchali S, Sharma A, Harrison J, Elhafsi A, Kang D, Pergament E, Cidon E, Katti S, Pavone M (2021) Network offloading policies for cloud robotics: a learning-based approach. Auton Robots 45(7):997–1012

    Article  Google Scholar 

  30. Alirezazadeh S, Alexandre LA (2023) Static algorithm allocation with duplication in robotic network cloud systems. IEEE Trans Parallel Distrib Syst 34(6):1897–1908

    Google Scholar 

  31. Chen W, Yaguchi Y, Naruse K, Watanobe Y, Nakamura K (2021) Qos-aware robotic streaming workflow allocation in cloud robotics systems. IEEE Trans Serv Comput 14(2):544–558

    Article  Google Scholar 

  32. Elfaki AO, Abduljabbar M, Ali L, Alnajjar F, Mehiar D, Marei AM, Alhmiedat T, Al-Jumaily A (2023) Revolutionizing social robotics: a cloud-based framework for enhancing the intelligence and autonomy of social robots. Robotics 12(2)

  33. Jain S, Doriya R (2019) Security issues and solutions in cloud robotics: a survey, vol 922, CCIS. Springer, pp 64–76

  34. Yan M, Castro P, Cheng P, Vatche P, Vatche I (2016) Building a chatbot with serverless computing. In: Proceedings of the MOTA ’16. Association for Computing Machinery, New York

  35. Ouerhani N, Maalel A, Ben Ghézela H (2020) Spececa: a smart pervasive chatbot for emergency case assistance based on cloud computing. Clust Comput 23(4):2471–2482

    Article  Google Scholar 

  36. Di Nuovo A, Varrasi S, Lucas A, Conti D, McNamara J, Soranzo A (2019) Assessment of cognitive skills via human-robot interaction and cloud computing. J Bionic Eng 16(3):526–539

    Article  Google Scholar 

  37. Baxter P, Del Duchetto F, Hanheide M (2020) Engaging learners in dialogue interactivity development for mobile robots. Adv Intell Syst Comput (AISC) 946:147–160

    Article  Google Scholar 

  38. Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D (2020) Language models are few-shot learners. In: Proceedings of the NIPS’20. Curran Associates Inc, Red Hook

  39. Lam M-L, Lam K-Y (2014) Path planning as a service ppaas: cloud-based robotic path planning. In Proceedings of the ROBIO 2014, Bali, Indonesia, pp 1839–1844

  40. Riazuelo L, Tenorth M, Di Marco D, Salas M, Gálvez-López D, Mösenlechner L, Kunze L, Beetz M, Tardós JD, Montano L, Montiel JMM (2015) Roboearth semantic mapping: a cloud enabled knowledge-based approach. Trans Autom 12:432–443

    Google Scholar 

  41. Joo S-H, Manzoor S, Rocha YG, Lee H-U, Kuc T-Y (2019) A realtime autonomous robot navigation framework for human like high-level interaction and task planning in global dynamic environment. arXiv:1905.12942

  42. Singhal A, Pallav P, Kejriwal N, Choudhury S, Kumar S, Sinha R (2017) Managing a fleet of autonomous mobile robots (amr) using cloud robotics platform. In ECMR 2017:1–6

    Google Scholar 

  43. Zagradjanin N, Pamucar D, Jovanovic K (2019) Cloud-based multi-robot path planning in complex and crowded environment with multi-criteria decision making using full consistency method. Symmetry 11(10)

  44. Bozcuoğlu AK, Kazhoyan G, Furuta Y, Stelter S, Beetz M, Okada K, Inaba M (2018) The exchange of knowledge using cloud robotics. IEEE Robot Autom Lett 3(2):1072–1079

    Article  Google Scholar 

  45. Chibani A, Amirat Y, Mohammed S, Matson E, Hagita N, Barreto M (2013) Ubiquitous robotics: recent challenges and future trends. Robot Auton Syst 61(11):1162–1172

    Article  Google Scholar 

  46. Sandygulova A, Swords D, Abdel-Naby S, O’Hare G, Dragone M (2013) A study of effective social cues within ubiquitous robotics. In HRI 2013:221–222

    Google Scholar 

  47. Bonaccorsi M, Fiorini L, Cavallo F, Saffiotti A, Dario P (2016) A cloud robotics solution to improve social assistive robots for active and healthy aging. Int J Soc Robot 8(3):393–408

    Article  Google Scholar 

  48. Soyata T, Muraleedharan R, Funai C, Kwon M, Heinzelman W (2012) Cloud-vision: real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. ISCC 2012:59–66

    Google Scholar 

  49. Pawle AA, Pawar PV (2013) Face recognition system (frs) on cloud computing for user authentication. Int J Soft Comput 3(4):189–192

    Google Scholar 

  50. Hossain MS, Muhammad G (2015) Cloud-assisted speech and face recognition framework for health monitoring. Mobile Netw Appl 20(3):391–399

    Article  Google Scholar 

  51. Muhammad G (2015) Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Cluster Comput 18(2):795–802

    Article  MathSciNet  Google Scholar 

  52. Di Nuovo A, Varrasi S, Lucas A, Conti D, McNamara J, Soranzo A (2019) Assessment of cognitive skills via human-robot interaction and cloud computing. J Bionic Eng 16(3):526–539

    Article  Google Scholar 

  53. Obayashi K, Masuyama S (2020) Pilot and feasibility study on elderly support services using communicative robots and monitoring sensors integrated with cloud robotics. Clin Ther 42(2):364-371.e4

    Article  Google Scholar 

  54. Kaptein F, Kiefer B, Cully A, Celiktutan O, Bierman B, Rijgersberg-Peters R, Broekens J, Van Vught W, Van Bekkum M, Demiris Y, Neerincx MA (2022) A cloud-based robot system for long-term interaction: principles, implementation, lessons learned. ACM Trans Hum Robot Interact 11(1)

  55. Mavridis N (2015) A review of verbal and non-verbal human-robot interactive communication. Rob Auton Syst 63(P1):22–35

    Article  MathSciNet  Google Scholar 

  56. Sugiura K, Zettsu K (2015) Rospeex: a cloud robotics platform for human–robot spoken dialogues. In: Proceedings of the IEEE/RSJ IROS, vol 2015, pp 6155–6160

  57. Grassi L, Recchiuto CT, Sgorbissa A (2022) Knowledge triggering, extraction and storage via human–robot verbal interaction. Rob Auton Syst 148:103938

    Article  Google Scholar 

  58. Black S, Biderman S, Hallahan E, Anthony Q, Gao L, Golding L, He H, Leahy C, McDonell K, Phang J, Pieler M, Sai Prashanth US, Purohit S, Reynolds L, Tow J, Wang B, Weinbach S (2022) Gpt-neox-20b: an open-source autoregressive language model. arXiv

  59. McGuffie K, Newhouse A (2020) The radicalization risks of GPT-3 and advanced neural language models

  60. Chen YF, Everett M, Liu M, How JP (2017) Socially aware motion planning with deep reinforcement learning. In: Proceedings of the IEEE/RSJ IROS 2017, vol 2017, September, pp 1343–1350

  61. Lemaignan S, Warnier M, Sisbot EA, Clodic A, Alami R (2017) Artificial cognition for social human–robot interaction: an implementation. Artif Intell 247:45–69

    Article  MathSciNet  Google Scholar 

  62. Gerevini AE, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: Pddl3 and experimental evaluation of the planners. Artif Intell 173(5–6):619–668

  63. Chaisiriprasert P, Yongsiriwit K, Dailey MN, Anutariya C (2021) Ontology-based framework for cooperative learning of 3d object recognition. Appl Sci 11(17)

  64. Guarino N (1998) Formal ontology and information systems. In: Proceedings of the FOIS’98, Trento, pp 81–97

  65. Masse M (2011) REST API design rulebook: designing consistent RESTful web service interfaces. O’Reilly, Sebastopol

    Google Scholar 

  66. Erol K, Hendler J, Nau DS (1994) HTN planning: complexity and expressivity. In: Proceedingsof the AAAI 1994, pp 1123–1128

  67. Hayes B, Scassellati B (2016) Autonomously constructing hierarchical task networks for planning and human–robot collaboration. In: Proceedings IEEE ICRA 2016

  68. Carrithers M, Candea M, Sykes K, Holbraad M, Venkatesan S (2010) Ontology is just another word for culture: Motion tabled at the 2008 meeting of the group for debates in anthropological theory. Crit Anthropol 30(2):152–200

    Article  Google Scholar 

  69. Peng Z, Mo K, Zhu X, Chen J, Chen Z, Xu Q, Ma X (2020) Understanding user perceptions of robot’s delay, voice quality-speed trade-off and gui during conversation. In: CHI 2020, CHI EA ’20. Association for Computing Machinery, New York, pp 1–8

  70. Shiwa T, Kanda T, Imai M, Ishiguro H, Hagita N (2008) How quickly should communication robots respond? In: HRI, 2008, pp 153–160

  71. Brajesh D (2017) API management: an architect’s guide to developing and managing APIs for your organization, chapter API testing strategy. Springer, pp 153–164

  72. Halili HE (2008) Apache J Meter. Packt Publishing, Birmingham

    Google Scholar 

  73. Jha N, Popli R (2017) Comparative analysis of web applications using jmeter. Int J Adv Res Comput Sci 8(3):774–777

    Google Scholar 

  74. Wang J, Wu J (2019) Research on performance automation testing technology based on jmeter. In: Proceedings of the ICRIS 2019, pp 55–58

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lucrezia Grassi.

Ethics declarations

Conflict of interest

the authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication.

Ethics approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grassi, L., Recchiuto, C.T. & Sgorbissa, A. Sustainable cloud services for verbal interaction with embodied agents. Intel Serv Robotics 16, 599–618 (2023). https://doi.org/10.1007/s11370-023-00485-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-023-00485-3

Keywords