Abstract
Recently, collaborative robots, such as collaborative delivery robots, have been expected to improve the work efficiency of users. For natural human-robot collaboration, it is necessary to infer the appropriate goal position to transport instruments, where the user’s convenience and the surrounding environment are considered. In conventional research, the goal is inferred by demonstrating the user’s desired positions, but position demonstration requires many trials to obtain the inference model, which is burdensome for the user. Therefore, we focus on the user’s correction of the robot position and generate multiple position samples from the user’s corrective path. In addition, these position samples are weighted based on the implicit intention of the correction to learn both the desired and undesired positions. Consequently, the robot improves goal inference in fewer trials. The effectiveness of the proposed method was evaluated by experiment that simulated human-robot collaborative environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sierra Marín, S.D., et al.: Expectations and perceptions of healthcare professionals for robot deployment in hospital environments during the covid-19 pandemic. Front. Robot. AI 8, 102 (2021)
Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T.: A human aware mobile robot motion planner. IEEE Trans. Robot. 23(5), 874–883 (2007)
Che, Y., Okamura, A.M., Sadigh, D.: Efficient and trustworthy social navigation via explicit and implicit robot-human communication. IEEE Trans. Robot. 36(3), 692–707 (2020)
Kollmitz, M., Koller, T., Boedecker, J., Burgard, W.: Learning human-aware robot navigation from physical interaction via inverse reinforcement learning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11025–11031 (2020)
Abdo, N., Stachniss, C., Spinello, L., Burgard, W.: Robot, organize my shelves! Tidying up objects by predicting user preferences. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1557–1564 (2015)
Asama, H.: Robotics in Heisei: a review: trends and future prospects of robot technology. J. Jpn. Soc. Precis. Eng. 86(1), 23–27 (2020) (Japanese)
Bajcsy, A., Losey, D.P., O’Malley, M.K., Dragan, A.D.: Learning robot objectives from physical human interaction. In: Conference on Robot Learning (CoRL) (2017)
Cosgun, A., Christensen, H.I.: Context-aware robot navigation using interactively built semantic maps. Paladyn J. Behav. Robot. 9(1), 254–276 (2018)
Lindner, F.: A conceptual model of personal space for human-aware robot activity placement. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5770–5775 (2015)
Kawasaki, Y., Takahashi, M.: Bottom-up action modeling via spatial factorization for serving food. Adv. Robot. 35(12), 756–770 (2021)
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969 (2017)
Taylor, C.C.: Automatic bandwidth selection for circular density estimation. Comput. Stat. Data Anal. 52(7), 3493–3500 (2008)
Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York (1992)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)
Vicon Motion Systems Ltd.: VICON. http://vicon.com (as of Apr 2022)
Acknowledgments
This work was supported by Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) [grant number JPMJCR19A1].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ohnishi, F., Kawasaki, Y., Takahashi, M. (2023). Goal Inference via Corrective Path Demonstration for Human-Robot Collaboration. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-22216-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22215-3
Online ISBN: 978-3-031-22216-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)