Abstract
We present the robotic system IRMA (Interactive Robotic Memory Aid) that assists humans in their search for misplaced belongings within a natural home-like environment. Our stand-alone system integrates state-of-the-art approaches in a novel manner to achieve a seamless and intuitive human-robot interaction. IRMA directs its gaze toward the speaker and understands the person’s verbal instructions independent of specific grammatical constructions. It determines the positions of relevant objects and navigates collision-free within the environment. In addition, IRMA produces natural language descriptions for the objects’ positions by using furniture as reference points. To evaluate IRMA’s usefulness, a user study with 20 participants has been conducted. IRMA achieves an overall user satisfaction score of 4.05 and a perceived accuracy rating of 4.15 on a scale from 1–5 with 5 being the best.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
A video showing the robot’s performance is presented in the video session of the IEEE RO-MAN 2016 conference [29].
- 2.
Our dataset is available at https://figshare.com/s/d949d3410df8db468f77 [30].
References
Barla, A., Odone, F., Verri, A.: Histogram intersection kernel for image classification. In: International Conference on Image Processing (ICIP), vol. 3, pp. 513–516. IEEE (2003)
Bartneck, C., Kulić, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Social Robot. 1(1), 71–81 (2008)
Beer, J.M., Smarr, C.A., Chen, T.L., Prakash, A., Mitzner, T.L., Kemp, C.C., Rogers, W.A.: The domesticated robot: design guidelines for assisting older adults to age in place. In: Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2012, pp. 335–342. ACM/IEEE (2012)
Bohren, J., Cousins, S.: The SMACH high-level executive. IEEE Robot. Autom. Mag. 17(4), 18–20 (2010)
Bohren, J., Rusu, R.B., Jones, E.G., Marder-Eppstein, E., Pantofaru, C., Wise, M., Mösenlechner, L., Meeussen, W., Holzer, S.: Towards autonomous robotic butlers: lessons learned with the PR2. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5568–5575. IEEE (2011)
Brooke, J.: SUS - a quick and dirty usability scale. In: Usability Evaluation in Industry, pp. 189–194. Taylor & Francis (1996)
Brooke, J.: SUS: a retrospective. J. Usability Stud. 8(2), 29–40 (2013)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Deyle, T., Reynolds, M.S., Kemp, C.C.: Finding and navigating to household objects with UHF RFID tags by optimizing RF signal strength. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2579–2586, September 2014
Fasola, J., Mataric, M.: A socially assistive robot exercise coach for the elderly. J. Hum.-Robot Interact. 2(2), 3–32 (2013)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 524–531. IEEE (2005)
Foster, M.E., Gaschler, A., Giuliani, M., Isard, A., Pateraki, M., Petrick, R.P.: Two people walk into a bar: dynamic multi-party social interaction with a robot agent. In: ACM International Conference on Multimodal Interaction, pp. 3–10. ICMI, ACM (2012)
Fox, D.: Adapting the sample size in particle filters through KLD-sampling. Int. J. Robot. Res. 22(12), 985–1003 (2003)
Graf, B., Reiser, U., Hägele, M., Mauz, K., Klein, P.: Robotic home assistant care-O-bot 3 - product vision and innovation platform. In: IEEE Workshop on Advanced Robotics and its Social Impacts, pp. 139–144. IEEE (2009)
Guo, B., Imai, M.: Home-explorer: search, localize and manage the physical artifacts indoors. In: International Conference on Advanced Information Networking and Applications (AINA), pp. 378–385. IEEE (2007)
Hinaut, X., Petit, M., Pointeau, G., Dominey, P.F.: Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks. Front. Neurorobotics 8, 16 (2014)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2169–2178. IEEE (2006)
Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)
Likert, R.: A Technique for the Measurement of Attitudes. Archives of Psychology (1932)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Murray, J., Wermter, S., Erwin, H.: Auditory robotic tracking of sound sources using hybrid cross-correlation and recurrent networks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3554–3559. IEEE (2005)
Palmer, M., Gildea, D., Xue, N.: Semantic role labeling. Synth. Lect. Hum. Lang. Technol. 3(1), 1–103 (2010)
Parisi, G.I., Bauer, J., Strahl, E., Wermter, S.: A multi-modal approach for assistive humanoid robots. In: Workshop on Multimodal and Semantics for Robotics Systems (MuSRobS), vol. 1540, pp. 10–15. CEUR Workshop Proceedings (2015)
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 6 (2009)
Rosenthal-von der Pütten, A.M., Krämer, N.C.: How design characteristics of robots determine evaluation and uncanny valley related responses. Comput. Hum. Behav. 36, 422–439 (2014)
Twiefel, J., Baumann, T., Heinrich, S., Wermter, S.: Improving domain-independent cloud-based speech recognition with domain-dependent phonetic post-processing. In: AAAI Conference on Artificial Intelligence, vol. 28, pp. 1529–1535. AAAI Press (2014)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518. IEEE (2001)
Wieser, I., Toprak, S., Grenzing, A., Hinz, T., Auddy, S., Karaoğuz, E.C., Chandran, A., Remmels, M., El Shinawi, A., Josifovski, J., Vankadara, L.C., Ul Wahab, F., Bahnemiri, A.M., Sahu, D., Heinrich, S., Navarro-Guerrero, N., Strahl, E., Twiefel, J., Wermter, S.: A Robotic Home Assistant with Memory Aid Functionality, video. IEEE RO-MAN 2016. https://www.informatik.uni-hamburg.de/wtm/videos/VideoSubmission_UniHamburgWTM_RO-MAN2016.mp.4. Accepted May 2016
Wieser, I., Toprak, S., Grenzing, A., Hinz, T., Auddy, S., Karaoğuz, E.C., Chandran, A., Remmels, M., El Shinawi, A., Josifovski, J., Vankadara, L.C., Ul Wahab, F., Bahnemiri, A.M., Sahu, D., Heinrich, S., Navarro-Guerrero, N., Strahl, E., Twiefel, J., Wermter, S.: Dataset for “A Robotic Home Assistant with Memory Aid Functionality”, May 2016. https://figshare.com/s/d949d3410df8db468f77
Acknowledgments
The authors gratefully acknowledge partial support from the German Research Foundation DFG under project CML (TRR 169), the European Union under project SECURE (No 642667), and the Hamburg Landesforschungsförderungsprojekt.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wieser, I. et al. (2016). A Robotic Home Assistant with Memory Aid Functionality. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-46073-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46072-7
Online ISBN: 978-3-319-46073-4
eBook Packages: Computer ScienceComputer Science (R0)