Abstract
The evaluation and manipulation of robot motion are essential for effective motion planning. Similar to audio and video data, motion data requires transform operations and a merit function when utilizing supervised and semi-supervised algorithms. However, designing an appropriate evaluation method remains a challenge, and motion data cannot be processed with acceleration. This paper proposes a method for calculating robot posture similarity and action sequence similarity while introducing a method for scaling action sequences. First, a posture and action sequence similarity calculation method based on visual angle is proposed. Then, a method for scaling action sequences is designed, drawing on resampling techniques from the audio field. Through a series of experimental analyses, the evaluation methods demonstrated consistency with human evaluations, and the distortion rate of action sequences at different scales was less than 1%. Applying the proposed method to robot motion planning problems allows for the implementation of supervisory algorithms, resulting in intelligent robot motion planning.
This work was supported by the Open Project of Scientific Research Platform of Grain Information Processing Center of Henan University of Technology (KFJJ-2021-107, KFJJ-2021-108), the Key Scientific Research Projects of Higher Education Institutions in Henan Province (No. 23B520001), the Science and Technology Research Project of Henan Province under Grant 222102210108, the Science and Technology Research and Development Plan Joint fund (application research) project of Henan Province (222103810042), and the Henan Science and technology research project (222102210309).
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References
Bi, T., Fankhauser, P., Bellicoso, D., Hutter, M.: Real-time dance generation to music for a legged robot. In: IEEE International Conference on Intelligent Robots and Systems, pp. 1038–1044 (2018)
Chen, B., Su, J., Wang, L., Gu, Q.: Learning robot grasping from a random pile with deep Q-learning. In: Liu, X.-J., Nie, Z., Yu, J., Xie, F., Song, R. (eds.) ICIRA 2021. LNCS (LNAI), vol. 13014, pp. 142–152. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89098-8_14
Ehrlich, S.K., Cheng, G.: A feasibility study for validating robot actions using EEG-based error-related potentials. Int. J. Soc. Robot. 11, 271–283 (2019)
Feng, C., Lan, X., Wan, L., Liang, Z., Wang, H.: A guided evaluation method for robot dynamic manipulation. In: Chan, C.S., et al. (eds.) ICIRA 2020. LNCS (LNAI), vol. 12595, pp. 161–170. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66645-3_14
Fu, T., Li, F., Zheng, Y., Song, R.: Process learning of robot fabric manipulation based on composite reward functions. In: Liu, X.-J., Nie, Z., Yu, J., Xie, F., Song, R. (eds.) ICIRA 2021. LNCS (LNAI), vol. 13014, pp. 163–174. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89098-8_16
Johannink, T., et al.: Residual reinforcement learning for robot control. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 6023–6029. IEEE (2019)
Kattepur, A.: Roboplanner: autonomous robotic action planning via knowledge graph queries. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 953–956 (2019)
Liu, N., Liu, Z., Cui, L.: A modified cartesian space DMPS model for robot motion generation. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds.) ICIRA 2019. LNCS (LNAI), vol. 11745, pp. 76–85. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27529-7_7
Losey, D.P., Srinivasan, K., Mandlekar, A., Garg, A., Sadigh, D.: Controlling assistive robots with learned latent actions. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 378–384. IEEE (2020)
Manfrè, A., Infantino, I., Vella, F., Gaglio, S.: An automatic system for humanoid dance creation. Biol. Inspired Cogn. Archit. 15, 1–9 (2016)
Menolotto, M., Komaris, D.S., Tedesco, S., O’Flynn, B., Walsh, M.: Motion capture technology in industrial applications: a systematic review. Sensors 20(19), 5687 (2020)
Özen, F., Küntan, U., Tükel, D.B.: Robot-music synchronization: self-designed dance. In: IEEE EUROCON 2017-17th International Conference on Smart Technologies, pp. 582–587. IEEE (2017)
Peng, H., Li, J., Hu, H., Zhao, L., Feng, S., Hu, K.: Feature fusion based automatic aesthetics evaluation of robotic dance poses. Robot. Auton. Syst. 111, 99–109 (2019)
Peng, W.: A robot dance generation system based on deep learning method. Master’s thesis, Xiamen University (2019)
Phueakthong, P., Varagul, J.: A development of mobile robot based on ROS2 for navigation application. In: 2021 International Electronics Symposium (IES), pp. 517–520. IEEE (2021)
Ravichandar, H., Polydoros, A.S., Chernova, S., Billard, A.: Recent advances in robot learning from demonstration. Annu. Rev. Control Robot. Auton. Syst. 3, 297–330 (2020)
Sunardi, M., Perkowski, M.: Music to motion: using music information to create expressive robot motion. Int. J. Soc. Robot. 10(1), 43–63 (2018)
Wu, R., et al.: Towards deep learning based robot automatic choreography system. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds.) ICIRA 2019. LNCS (LNAI), vol. 11743, pp. 629–640. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27538-9_54
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Wu, R. et al. (2023). An Action Evaluation and Scaling Algorithm for Robot Motion Planning. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14270. Springer, Singapore. https://doi.org/10.1007/978-981-99-6492-5_34
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