Abstract
Traditional robot programming is often not feasible in small-batch production, as it is time-consuming, inefficient, and expensive. To shorten the time necessary to deploy robot tasks, we need appropriate tools to enable efficient reuse of existing robot control policies. Incremental Learning from Demonstration (iLfD) and reversible Dynamic Movement Primitives (DMP) provide a framework for efficient policy demonstration and adaptation. In this paper, we extend our previously proposed framework with improvements that provide better performance and lower the algorithm’s computational burden. Further, we analyse the learning stability and evaluate the proposed framework with a comprehensive user study. The proposed methods have been evaluated on two popular collaborative robots, Franka Emika Panda and Universal Robot UR10.
Materials Availability
The data that support the findings of this study are available from the corresponding author, B.N., upon reasonable request.
References
Molina, E., Lazaro, O., Sepulcre, M., Gozalvez, J., Passarella, A., Raptis, T.P., Ude, A., Nemec, B., Rooker, M., Kirstein, F., Mooij, E.: The AUTOWARE framework and requirements for the cognitive digital automation. In: Camarinha-Matos, L, Afsarmanesh, H, Fornasiero, R (eds.) IFIP Advances in Information and Communication Technology: Volume 506. Springer International Publishing, Cham (2017)
Gašpar, T., Deniša, M., Radanovič, P., Ridge, B., Savarimuthu, T.R., Kramberger, A., Priggemeyer, M., Rossmann, J., Wörgötter, F., Ivanovska, T., Parizi, S., Gosar, Z., Kovač, I., Ude, A.: Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcells. Robot. Comput. Integr. Manuf. 66, 101979 (2020)
Dean-Leon, E., Ramirez-Amaro, K., Bergner, F., Dianov, I., Lanillos, P., Cheng, G.: Robotic technologies for fast deployment of industrial robot systems. IECON Proceedings (Industrial Electronics Conference), pp. 6900–6907 (2016)
Dillmann, R.: Teaching and learning of robot tasks via observation of human performance. Robot. Auton. Syst. 47(2-3), 109–116 (2004)
Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Robot Programming by Demonstration. In: Siciliano, B, Khatib, O (eds.) Springer handbook of robotics, pp. 1371–1394. Springer, Berlin, Heidelberg (2008)
Argall, B., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)
Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., Schaal, S.: Skill learning and task outcome prediction for manipulation. IEEE International Conference on Robotics and Automation (ICRA), pp. 3828–3834 (2011)
Peters, J., Mülling, K, Kober, J.: Towards motor skill learning for robotics. In: Pradalier, C, Siegwart, R, Hirzinger, G (eds.) Robotics research, pp. 469–482. Springer Verlag, Berlin, Heidelberg (2011)
Bristow, D.A., Tharayil, M., Alleyne, A.G.: A Survey of Iterative Learning Control - A learning-based method for high-performance tracking control. IEEE control systems magazine 26(3), 96–114 (2006)
Calinon, S., Billard, A.: Incremental learning of gestures by imitation in a humanoid robot. In: 2nd ACM/IEEE International conference on human-robot interaction (HRI), pp. 255–262 (2007)
Kulic, D., Takano, W., Nakamura, Y.: Combining automated on-line segmentation and incremental clustering for whole body motions. In: IEEE International conference on robotics and automation (ICRA), pp. 2591–2598. Pasadena, CA (2008)
Gams, A., Petrič, T., Do, M., Nemec, B., Morimoto, J., Asfour, T., Ude, A.: Adaptation and coaching of periodic motion primitives through physical and visual interaction. Robot. Auton. Syst. 75, 340–351 (2016)
Lee, D., Ott, C.: Incremental motion primitive learning by physical coaching using impedance control. In: IEEE/RSJ International conference on intelligent robots and systems (IROS), pp. 4133–4140, Taipei, Taiwan (2010)
Ewerton, M., Maeda, G., Kollegger, G., Wiemeyer, J., Peters, J.: Incremental imitation learning of context-dependent motor skills. In: IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 351–358. Cancun, Mexico (2016)
Pardowitz, M., Knoop, S., Dillmann, R., Zollner, R.D.: Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37(2), 322–332 (2007)
Tykal, M., Montebelli, A., Kyrki, V.: Incrementally assisted kinesthetic teaching for programming by demonstration. In: 11th ACM/IEEE International conference on human-robot interaction (HRI), pp. 205–212 (2016)
žlajpah, L., Petrič, T.: Unified Virtual Guides Framework for Path Tracking Tasks. Robotica 38(10), 1807–1823 (2020)
Papageorgiou, D., Kastritsi, T., Doulgeri, Z.: A passive robot controller aiding human coaching for kinematic behavior modifications. Robot. Comput. Integr. Manuf. 61, 101824 (2020)
Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47(6), 381–391 (1954)
Nemec, B., Likar, N., Gams, A., Ude, A.: Human robot cooperation with compliance adaptation along the motion trajectory. Auton. Robot. 42(5), 1023–1035 (2018)
Nemec, B., žlajpah, L., Šlajpah, S., Piškur, J., Ude, A.: An efficient PbD framework for fast deployment of bi-manual assembly tasks. In: IEEE-RAS International conference on humanoid robots (Humanoids), pp. 166–173. Beijing, China (2018)
Nemec, B., Simonič, M., Petrič, T., Ude, A.: Incremental policy refinement by recursive regression and kinesthetic guidance. In: 19th International conference on advanced robotics (ICAR), pp. 344–349. Belo Horizonte, Brazil (2019)
Gašpar, T., Nemec, B., Morimoto, J., Ude, A.: Skill learning and action recognition by arc-length dynamic movement primitives. Robot. Auton. Syst. 100, 225–235 (2018)
Abu-Dakka, F.J., Nemec, B., Jorgensen, J.A., Savarimuthu, T.R., Krüger, N., Ude, A.: Adaptation of Manipulation Skills in Physical Contact with the Environment to Reference Force Profiles. Auton. Robot. 39(2), 199–217 (2015)
Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: Learning attractor models for motor behaviors. Neural Comput. 25(2), 328–73 (2013)
Vuga, R., Nemec, B., Ude, A.: Speed adaptation for self-improvement of skills learned from user demonstrations. Robotica 34(12), 2806–2822 (2016)
Ude, A., Nemec, B., Petrič, T., Morimoto, J.: Orientation in Cartesian space dynamic movement primitives. In: IEEE International conference on robotics and automation (ICRA), pp. 2997–3004. Hong Kong, China (2014)
Koutras, L., Doulgeri, Z.: A correct formulation for the orientation dynamic movement primitives for robot control in the cartesian space. In: Proc. conference on robot learning (CoRL), pp. 293–302. Osaka, Japan (2019)
Iturrate, I., Sloth, C., Kramberger, A., Petersen, H.G., Østergaard, E.H., Savarimuthu, T.R.: Towards reversible dynamic movement primitives. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5063–5070. Macau, China (2019)
Ravani, R., Meghdari, A.: Velocity distribution profile for robot arm motion using rational Frenet-Serret curves. Informatica 17(1), 69–84 (2006)
Khatib, O.: Augmented object and reduced effective inertia in robot systems. In: American control conference, pp. 2140–2147. Atlanta, GA (1988)
Nemec, B., Petrič, T., Ude, A.: Force adaptation with recursive regression Iterative Learning Controller. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2835–2841. Hamburg, Germany (2015)
Khatib, O.: A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE J. Robot. Autom. 3, 43–53 (1987)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE/RSJ International conference on intelligent robots and systems (IROS), pp. 573–580 (2012)
Funding
The research leading to these results has received funding from the Horizon 2020 RIA Programme grant 820767 CoLLaboratE and from the program group P2-0076 Automation, robotics, and biocybernetics funded by the Slovenian Research Agency.
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Conceptualization, B.N. and M.S.; methodology, B.N., M.S., A.U. and T.P.; software, M.S. and B.N.; formal analysis, B.N., M.S., A.U. and T.P.; investigation, M.S.; data curation, M.S. and T.P.; writing–original draft preparation, B.N.; writing–review and editing, M.S., B.N. and A.U.; visualization, M.S. and B.N.; supervision, B.N. and A.U.; funding acquisition, B.N. and A.U. All authors read and approved the final manuscript.
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Simonič, M., Petrič, T., Ude, A. et al. Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance. J Intell Robot Syst 102, 5 (2021). https://doi.org/10.1007/s10846-021-01328-y
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DOI: https://doi.org/10.1007/s10846-021-01328-y
Keywords
- Incremental learning
- Coaching
- Stability analysis
- User study