SCIENCE CHINA Information Sciences, Volume 63 , Issue 7 : 170201(2020) https://doi.org/10.1007/s11432-019-2748-x

Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

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  • ReceivedJun 18, 2019
  • AcceptedNov 29, 2019
  • PublishedMay 26, 2020



This work was supported by National Natural Science Foundation of China (Grant Nos. 61861136009, 61811530281).


[1] Peternel L, Petri? T, Oztop E. Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Auton Robot, 2014, 36: 123-136 CrossRef Google Scholar

[2] Li Y, Tee K P, Yan R. A framework of human-robot coordination based on game theory and policy iteration. IEEE Trans Robot, 2016, 32: 1408-1418 CrossRef Google Scholar

[3] Li Y, Tee K P, Yan R, et al. Adaptive optimal control for coordination in physical human-robot interaction. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. 20--25. Google Scholar

[4] Li Y, Ge S S. Human-robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron, 2014, 19: 1007-1014 CrossRef Google Scholar

[5] Wu Y, Wang R, D'Haro L F, et al. Multi-modal robot apprenticeship: imitation learning using linearly decayed DMP+ in a human-robot dialogue system. In: Proceeding of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. 1--7. Google Scholar

[6] Wang R, Wu Y, Chan W L, et al. Dynamic movement primitives plus: for enhanced reproduction quality and efficient trajectory modification using truncated kernels and local biases. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016. 3765--3771. Google Scholar

[7] Chen F, Sekiyama K, Cannella F. Optimal subtask allocation for human and robot collaboration within hybrid assembly system. IEEE Trans Automat Sci Eng, 2014, 11: 1065-1075 CrossRef Google Scholar

[8] Ko W K H, Wu Y, Tee K P, et al. Towards industrial robot learning from demonstration. In: Proceedings of the 3rd International Conference on Human-Agent Interaction. ACM, 2015. 235--238. Google Scholar

[9] He W, Dong Y, Sun C. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybern Syst, 2016, 46: 334-344 CrossRef Google Scholar

[10] Denisa M, Gams A, Ude A. Learning compliant movement primitives through demonstration and statistical generalization. IEEE/ASME Trans Mechatron, 2016, 21: 2581-2594 CrossRef Google Scholar

[11] Yang C, Ganesh G, Haddadin S. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans Robot, 2011, 27: 918-930 CrossRef Google Scholar

[12] Burdet E, Ganesh G, Yang C, et al. Interaction force, impedance and trajectory adaptation: by humans, for robots. In: Experimental Robotics. Berlin, Heidelberg: Springer, 2014. 331--345. Google Scholar

[13] Ficuciello F, Villani L, Siciliano B. Variable impedance control of redundant manipulators for intuitive human-robot physical interaction. IEEE Trans Robot, 2015, 31: 850-863 CrossRef Google Scholar

[14] He W, Dong Y. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learning Syst, 2018, 29: 1174-1186 CrossRef PubMed Google Scholar

[15] He W, Meng T, He X. Iterative learning control for a flapping wing micro aerial vehicle under distributed disturbances. IEEE Trans Cybern, 2019, 49: 1524-1535 CrossRef PubMed Google Scholar

[16] Liang X, Zhao H, Li X. Force tracking impedance control with unknown environment via an iterative learning algorithm. Sci China Inf Sci, 2019, 62: 050215 CrossRef Google Scholar

[17] Liang X, Zhao H, Li X, et al. Force tracking impedance control with unknown environment via an iterative learning algorithm. Sci China Inf Sci 2019, 62: 050215. Google Scholar

[18] Li Z, Huang Z, He W. Adaptive impedance control for an upper limb robotic exoskeleton using biological signals. IEEE Trans Ind Electron, 2017, 64: 1664-1674 CrossRef Google Scholar

[19] Boaventura T, Buchli J, Semini C. Model-based hydraulic impedance control for dynamic robots. IEEE Trans Robot, 2015, 31: 1324-1336 CrossRef Google Scholar

[20] Roveda L, Iannacci N, Vicentini F. Optimal impedance force-tracking control design with impact formulation for interaction tasks. IEEE Robot Autom Lett, 2016, 1: 130-136 CrossRef Google Scholar

[21] Yang C, Zeng C, Fang C. A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills. IEEE/ASME Trans Mechatron, 2018, 23: 1193-1203 CrossRef Google Scholar

[22] Yang C, Zeng C, Cong Y. A learning framework of adaptive manipulative skills from human to robot. IEEE Trans Ind Inf, 2019, 15: 1153-1161 CrossRef Google Scholar

[23] Ajoudani A, Fang C, Tsagarakis N. Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation. Int J Robotics Res, 2018, 37: 155-167 CrossRef Google Scholar

[24] Buchli J, Stulp F, Theodorou E. Learning variable impedance control. Int J Robotics Res, 2011, 30: 820-833 CrossRef Google Scholar

[25] Calinon S, Kormushev P, Caldwell D G. Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning. Robotics Autonomous Syst, 2013, 61: 369-379 CrossRef Google Scholar

[26] Li Z, Zhao T, Chen F. Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator. IEEE/ASME Trans Mechatron, 2018, 23: 121-131 CrossRef Google Scholar

[27] Rozo L, Silvério J, Calinon S. Learning controllers for reactive and proactive behaviors in human-robot collaboration. Front Robot AI, 2016, 3 CrossRef Google Scholar

[28] Duan J, Ou Y, Xu S, et al. Learning Compliant Manipulation Tasks from Force Demonstrations. In: Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). IEEE, 2018. 449--454. Google Scholar

[29] Ganesh G, Albu-Sch??ffer A, Haruno M, et al. Biomimetic motor behavior for simultaneous adaptation of force, impedance and trajectory in interaction tasks. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation. IEEE, 2010. 2705--2711. Google Scholar

[30] Li Y, Ganesh G, Jarrasse N. Force, impedance, and trajectory learning for contact tooling and haptic identification. IEEE Trans Robot, 2018, 34: 1170-1182 CrossRef Google Scholar

  • Figure 2

    (Color online) The diagram of the biomimetic controller.

  • Figure 3

    (Color online) The set-up (a) and the results of the disturbance test of (b) joint S0, (c) joint S1, (d) joint E0, (e) joint E1, (f) joint W0, (g) joint W1, and (h) joint W2.

  • Figure 4

    (Color online) The experimental set-up for the sawing task. (a) The human partner actively pulls the saw. The impedance of the robotic arm increases gradually from a small value to some extent. (b) The robot actively pulls the saw back owing to the large impedance. The human partner relaxes his muscle strength in this phase.

  • Figure 5

    (Color online) The experimental results of the sawing task. (a) Joint S1; (b) joint E1; (c) joint W0; (d) joints S0, E0, W0, and W2.


    Algorithm 1 The online learning of the robotic compliant movement

    Require:The desired robot arm posture $(q_{0},~~\dot{q}_{0})$;

    Output: The computed joint torque command $~\tau_{c}^{t}~$ by (2) at each time step;

    Initialize the stiffness and damping matrix as $~K^{0}=\text{diag}\lbrace0,0,~\ldots,~0\rbrace~$ and $~D^{0}=\text{diag}\lbrace0,0,~\ldots,~0\rbrace~$;

    Initialize the feedforward vector as $~u^{0}=\text{diag}\lbrace0,0,~\ldots,~0\rbrace~$;

    Set the constant coefficients $\pi~$, $~\delta$, $a$, and $~b~$;

    Set the constant parametric vectors $~\alpha~$ and $~\beta~$;

    Set the stiffness range $~K^{\text{max}}~$ and $~K^{\text{min}}~$;

    for each time step $t~\in~[1,T]$

    Get the current robot joint states $~q~$ and $~\dot{q}~$;

    Compute the angle error and the velocity error according to (4);

    Compute the sliding error according to (8);

    Compute the vector $~\gamma~$ according to (16);

    Update the feedforward torque $~u^{t+1}=u^{t}~+~\Delta~u~$, using (15);

    Update the stiffness matrix $~K^{t+1}=K^{t}~+~\Delta~K~$, using (17);

    Adjust the stiffness values in a proper range based on (18);

    Compute the damping matrix $~D^{t+1}~$;

    Compute the impedance term $~v^{t+1}~$;

    Compute the joint torque $~\tau_{c}^{t+1}~$, using (2);

    Send the joint torque command to the robotic joint motors;

    end for