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Towards Partner-Aware Humanoid Robot Control Under Physical Interactions

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1038)

Abstract

The topic of physical human-robot interaction received a lot of attention from the robotics community because of many promising application domains. However, studying physical interaction between a robot and an external agent, like a human or another robot, without considering the dynamics of both the systems may lead to many shortcomings in fully exploiting the interaction. In this paper, we present a coupled-dynamics formalism followed by a sound approach in exploiting helpful interaction with a humanoid robot. In particular, we propose the first attempt to define and exploit the human help for the robot to accomplish a specific task. As a result, we present a task-based partner-aware robot control techniques. The theoretical results are validated by conducting experiments with two iCub humanoid robots involved in physical interaction.

Keywords

  • Physical robot-robot interaction
  • Physical human-robot interaction
  • Humanoids

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References

  1. Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A.: Collaborative human-humanoid carrying using vision and haptic sensing. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 607–612. IEEE (2014)

    Google Scholar 

  2. Samuel, A., Berniker, M., Herr, H.: Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits. Neural Netw. 21(4), 654–666 (2008)

    CrossRef  Google Scholar 

  3. Bussy, A., Gergondet, P., Kheddar, A., Keith, F., Crosnier, A.: Proactive behavior of a humanoid robot in a haptic transportation task with a human partner. In: RO-MAN, 2012 IEEE, pp. 962–967. IEEE (2012)

    Google Scholar 

  4. Bussy, A., Kheddar, A., Crosnier, A., Keith, F.: Human-humanoid haptic joint object transportation case study. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3633–3638. IEEE (2012)

    Google Scholar 

  5. Caux, S., Mateo, E., Zapata, R.: Balance of biped robots: special double-inverted pendulum. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics 1998, vol. 4, pp. 3691–3696. IEEE (1998)

    Google Scholar 

  6. Cho, E., Chen, R., Merhi, L.-K., Xiao, Z., Pousett, B., Menon, C.: Force myography to control robotic upper extremity prostheses: a feasibility study. Front. Bioeng. Biotechnol. 4, 18 (2016)

    CrossRef  Google Scholar 

  7. De Santis, A., Lippiello, V., Siciliano, B., Villani, L.: Human-robot interaction control using force and vision. In: Advances in Control Theory and Applications, pp. 51–70. Springer, Heidelberg (2007)

    Google Scholar 

  8. Donner, P., Buss, M.: Cooperative swinging of complex pendulum-like objects: experimental evaluation. IEEE Trans. Rob. 32(3), 744–753 (2016)

    CrossRef  Google Scholar 

  9. Featherstone, R.: Rigid Body Dynamics Algorithms. Springer-Verlag New York Inc., Secaucus (2007)

    MATH  Google Scholar 

  10. Goodrich, M.A., Schultz, A.C., et al.: Human-robot interaction: a survey. Found. Trends® Hum. Comput. Interact. 1(3), 203–275 (2008)

    MATH  CrossRef  Google Scholar 

  11. Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S.: Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 981–988. IEEE (2014)

    Google Scholar 

  12. Hirai, K., Hirose, M., Haikawa, Y., Takenaka, T.: The development of Honda humanoid robot. In: Proceedings of the 1998 IEEE International Conference on Robotics and Automation 1998, vol. 2, pp. 1321–1326. IEEE (1998)

    Google Scholar 

  13. Hofmann, A., Popovic, M., Herr, H.: Exploiting angular momentum to enhance bipedal center-of-mass control. In: IEEE International Conference on Robotics and Automation 2009, ICRA 2009, pp. 4423–4429. IEEE (2009)

    Google Scholar 

  14. Hyon, S.-H., Hale, J.G., Cheng, G., et al.: Full-body compliant human-humanoid interaction: balancing in the presence of unknown external forces. IEEE Trans. Robot. 23(5), 884–898 (2007)

    CrossRef  Google Scholar 

  15. Ikemoto, S., Amor, H.B., Minato, T., Jung, B., Ishiguro, H.: Physical human-robot interaction: mutual learning and adaptation. IEEE Robot. Autom. Mag. 19(4), 24–35 (2012)

    CrossRef  Google Scholar 

  16. Khalil, W., Dombre, E.: Modeling, Identification and Control of Robots. Butterworth-Heinemann, Oxford (2004)

    MATH  Google Scholar 

  17. Koolen, T., Bertrand, S., Thomas, G., De Boer, T., Tingfan, W., Smith, J., Englsberger, J., Pratt, J.: Design of a momentum-based control framework and application to the humanoid robot atlas. Int. J. Humanoid Rob. 13(01), 1650007 (2016)

    CrossRef  Google Scholar 

  18. Kyrkjebø, E.: Inertial human motion estimation for physical human-robot interaction using an interaction velocity update to reduce drift. In: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 163–164. ACM (2018)

    Google Scholar 

  19. Latella, C., Lorenzini, M., Lazzaroni, M., Romano, F., Traversaro, S., Akhras, M.A., Pucci, D., Nori, F.: Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms. Auton. Robots. 43, 1591–1603 (2018)

    CrossRef  Google Scholar 

  20. Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K.: A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction. Appl. Mech. Rev. 70(1), 010804 (2018)

    CrossRef  Google Scholar 

  21. Marsden, J.E., Ratiu, T.S.: Introduction to Mechanics and Symmetry: A Basic Exposition of Classical Mechanical Systems. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  22. Mattar, E.A., Al-Junaid, H.J., Al-Seddiqi, H.H.: Biomimetic based EEG learning for robotics complex grasping and dexterous manipulation. In: Biomimetic Prosthetics. InTech (2018)

    Google Scholar 

  23. McMullen, D.P., Hotson, G., Katyal, K.D., Wester, B.A., Fifer, M.S., McGee, T.G., Harris, A., Johannes, M.S., Vogelstein, R.J., Ravitz, A.D., et al.: Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic. IEEE Trans. Neural Syst. Rehabil. Eng. 22(4), 784–796 (2014)

    CrossRef  Google Scholar 

  24. Metta, G., Natale, L., Nori, F., Sandini, G., Vernon, D., Fadiga, L., Von Hofsten, C., Rosander, K., Lopes, M., Santos-Victor, J., et al.: The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Netw. 23(8–9), 1125–1134 (2010)

    CrossRef  Google Scholar 

  25. Natale, L., Bartolozzi, C., Pucci, D., Wykowska, A., Metta, G.: iCub: the not-yet-finished story of building a robot child. Sci. Robot. 2(13), eaaq1026 (2017)

    CrossRef  Google Scholar 

  26. Nava, G., Romano, F., Nori, F., Pucci, D.: Stability analysis and design of momentum-based controllers for humanoid robots. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 680–687. IEEE (2016)

    Google Scholar 

  27. Nori, F., Traversaro, S., Eljaik, J., Romano, F., Del Prete, A., Pucci, D.: iCub whole-body control through force regulation on rigid non-coplanar contacts. Front. Robot. AI 2(6), 18 (2015)

    Google Scholar 

  28. Ott, C., Roa, M.A., Hirzinger, G.: Posture and balance control for biped robots based on contact force optimization. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 26–33. IEEE (2011)

    Google Scholar 

  29. Pattacini, U., Nori, F., Natale, L., Metta, G., Sandini, G.: An experimental evaluation of a novel minimum-jerk Cartesian controller for humanoid robots. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1668–1674, October 2010

    Google Scholar 

  30. Peternel, L., Babič, J.: Learning of compliant human-robot interaction using full-body haptic interface. Adv. Robot. 27(13), 1003–1012 (2013)

    CrossRef  Google Scholar 

  31. Peternel, L., Tsagarakis, N., Caldwell, D., Ajoudani, A.: Robot adaptation to human physical fatigue in human-robot co-manipulation. Auton. Robots. 42, 1011–1021 (2018)

    CrossRef  Google Scholar 

  32. Pucci, D., Romano, F., Traversaro, S., Nori, F.: Highly dynamic balancing via force control. In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 141–141, November 2016

    Google Scholar 

  33. Radmand, A., Scheme, E., Englehart, K.: A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 662–667. IEEE (2014)

    Google Scholar 

  34. Rasouli, M., Chellamuthu, K., Cabibihan, J.-J., Kukreja, S.L.: Towards enhanced control of upper prosthetic limbs: a force-myographic approach. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 232–236. IEEE (2016)

    Google Scholar 

  35. Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)

    CrossRef  Google Scholar 

  36. Reily, B., Han, F., Parker, L.E., Zhang, H.: Skeleton-based bio-inspired human activity prediction for real-time human-robot interaction. Auton. Robots. 42(6), 1281–1298 (2018)

    CrossRef  Google Scholar 

  37. Romano, F., Nava, G., Azad, M., Čamernik, J., Dafarra, S., Dermy, O., Latella, C., Lazzaroni, M., Lober, R., Lorenzini, M., Pucci, D., Sigaud, O., Traversaro, S., Babič, J., Ivaldi, S., Mistry, M., Padois, V., Nori, F.: The CoDyCo project achievements and beyond: toward human aware whole-body controllers for physical human robot interaction. IEEE Robot. Autom. Lett. 3(1), 516–523 (2018)

    CrossRef  Google Scholar 

  38. Sarac, M., Koyas, E., Erdogan, A., Cetin, M., Patoglu, V.: Brain computer interface based robotic rehabilitation with online modification of task speed. In: 2013 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–7. IEEE (2013)

    Google Scholar 

  39. Song, R., Tong, K., Hu, X., Li, L., et al.: Assistive control system using continuous myoelectric signal in robot-aided arm training for patients after stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 16(4), 371–379 (2008)

    CrossRef  Google Scholar 

  40. Stephens, B.J., Atkeson, C.G.: Dynamic balance force control for compliant humanoid robots. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1248–1255. IEEE (2010)

    Google Scholar 

  41. Wensing, P.M., Orin, D.E.: Generation of dynamic humanoid behaviors through task-space control with conic optimization. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3103–3109. IEEE (2013)

    Google Scholar 

  42. Yap, H.K., Mao, A., Goh, J.CH., Yeow, C.-H.: Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 781–786. IEEE (2016)

    Google Scholar 

  43. Zhou, Y., Fang, Y., Zeng, J., Li, K., Liu, H.: A multi-channel EMG-driven FES solution for stroke rehabilitation. In: International Conference on Intelligent Robotics and Applications, pp. 235–243. Springer (2018)

    Google Scholar 

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Acknowledgments

This work is supported by PACE project, Marie Skłodowska-Curie grant agreement No. 642961 and An.Dy project which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 731540. The authors would like to thank Yue Hu, Stefano Dafarra, Giulio Romualdi and, Aiko Dinale for their support in conducting the experiments.

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Correspondence to Yeshasvi Tirupachuri .

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A Appendix: Sketch of Proof of Lemma 1

A Appendix: Sketch of Proof of Lemma 1

Proof: The stability of \(\widetilde{\chi }\) can be analyzed by considering the following Lyapunov function:

(25)

where \(K_d, K_p \in \mathbb {R}^{p \times p}\) are two symmetric, positive-definite matrices. Now, on differentiating (25) and using the robot dynamics (1b) along with the force decomposition (18) obtained through coupled-dynamics, we get:

$$\begin{aligned} \dot{\mathrm {V}} = - \ \widetilde{\chi }^T \ K_D \ \widetilde{\chi } \ + \widetilde{\chi }^T \ [ \ \alpha - \ max(0,\alpha )\ ] \ \widetilde{\chi }^{\parallel } \end{aligned}$$
(26)

where,

$$\begin{aligned} \dot{\mathrm {V}} = - \ \widetilde{\chi }^T \ K_D \ \widetilde{\chi } \quad \quad \forall \ \alpha > 0 \end{aligned}$$
$$\begin{aligned} \dot{\mathrm {V}} = - \ \widetilde{\chi }^T \ K_D \ \widetilde{\chi } \ + \widetilde{\chi }^T \ \alpha \ \widetilde{\chi }^{\parallel } \quad \quad \forall \ \alpha \le 0 \end{aligned}$$

The fact that the human joint torques help the robot to perform a control action is encompassed in the right-hand side of the above equation. The component of human joint torques projected in the direction parallel to the task i.e. \(\alpha \) makes the Lyapunov function decrease faster. Thus the control law (23) ensures that \(\dot{\mathrm {V}} \le 0\) which proves that the trajectories are globally bounded. From Lyapunov theory, as \(\dot{\mathrm {V}} \le 0 \) in the neighborhood of the point (0, 0) the equilibrium point (22) is stable. The complete proof of Lemma 1 is beyond the scope of this paper due to the space limitations and it will be presented in full in our forthcoming journal publication.

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Tirupachuri, Y. et al. (2020). Towards Partner-Aware Humanoid Robot Control Under Physical Interactions. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_78

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