Abstract
To improve the robotic flexibility and dexterity in a human-robot collaboration task, it is important to adapt the robot impedance in a real-time manner to its partner’s behavior. However, it is often quite challenging to achieve this goal and has not been well addressed yet. In this paper, we propose a bio-inspired approach as a possible solution, which enables the online adaptation of robotic impedance in the unknown and dynamic environment. Specifically, the bio-inspired mechanism is derived from the human motor learning, and it can automatically adapt the robotic impedance and feedforward torque along the motion trajectory. It can enable the learning of compliant robotic behaviors to meet the dynamic requirements of the interactions. In order to validate the proposed approach, an experiment containing an anti-disturbance test and a human-robot collaborative sawing task has been conducted.
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Peternel L, Petriˇc T, Oztop E, et al. 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
Li Y, Tee K P, Yan R, et al. A framework of human-robot coordination based on game theory and policy iteration. IEEE Trans Robot, 2016, 32: 1408–1418
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), 2015. 20–25
Li Y, Ge S S. Human-robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron, 2014, 19: 1007–1014
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), 2018. 1–7
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), 2016. 3765–3771
Chen F, Sekiyama K, Cannella F, et al. Optimal subtask allocation for human and robot collaboration within hybrid assembly system. IEEE Trans Automat Sci Eng, 2014, 11: 1065–1075
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, 2015. 235–238
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
Denisa M, Gams A, Ude A, et al. Learning compliant movement primitives through demonstration and statistical generalization. IEEE/ASME Trans Mechatron, 2016, 21: 2581–2594
Yang C, Ganesh G, Haddadin S, et al. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans Robot, 2011, 27: 918–930
Burdet E, Ganesh G, Yang C, et al. Interaction force, impedance and trajectory adaptation: by humans, for robots. In: Experimental Robotics. Berlin: Springer, 2014. 331–345
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
He W, Dong Y. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learn Syst, 2018, 29: 1174–1186
He W, Meng T, He X, et al. Iterative learning control for a flapping wing micro aerial vehicle under distributed disturbances. IEEE Trans Cybern, 2019, 49: 1524–1535
Zhao Y R, Song Z B, Ma T Y, et al. A stiffness-adaptive control system for nonlinear stiffness actuators. Sci China Inf Sci, 2019, 62: 050210
Liang X Q, Zhao H, Li X F, et al. Force tracking impedance control with unknown environment via an iterative learning algorithm. Sci China Inf Sci, 2019, 62: 050215
Li Z, Huang Z, He W, et al. Adaptive impedance control for an upper limb robotic exoskeleton using biological signals. IEEE Trans Ind Electron, 2017, 64: 1664–1674
Boaventura T, Buchli J, Semini C, et al. Model-based hydraulic impedance control for dynamic robots. IEEE Trans Robot, 2015, 31: 1324–1336
Roveda L, Iannacci N, Vicentini F, et al. Optimal impedance force-tracking control design with impact formulation for interaction tasks. IEEE Robot Autom Lett, 2016, 1: 130–136
Yang C, Zeng C, Fang C, et al. A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills. IEEE/ASME Trans Mechatron, 2018, 23: 1193–1203
Yang C, Zeng C, Cong Y, et al. A learning framework of adaptive manipulative skills from human to robot. IEEE Trans Ind Inf, 2019, 15: 1153–1161
Ajoudani A, Fang C, Tsagarakis N, et al. Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation. Int J Robot Res, 2018, 37: 155–167
Buchli J, Stulp F, Theodorou E, et al. Learning variable impedance control. Int J Robot Res, 2011, 30: 820–833
Calinon S, Kormushev P, Caldwell D G. Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning. Robot Autonom Syst, 2013, 61: 369–379
Li Z, Zhao T, Chen F, et al. Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator. IEEE/ASME Trans Mechatron, 2018, 23: 121–131
Rozo L, Silvério J, Calinon S, et al. Learning controllers for reactive and proactive behaviors in human-robot collaboration. Front Robot AI, 2016, 3: 30
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), 2018. 449–454
Ganesh G, Albu-Schäeffer 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, 2010. 2705–2711
Li Y, Ganesh G, Jarrasse N, et al. Force, impedance, and trajectory learning for contact tooling and haptic identification. IEEE Trans Robot, 2018, 34: 1170–1182
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This work was supported by National Natural Science Foundation of China (Grant Nos. 61861136009, 61811530281).
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Zeng, C., Yang, C. & Chen, Z. Bio-inspired robotic impedance adaptation for human-robot collaborative tasks. Sci. China Inf. Sci. 63, 170201 (2020). https://doi.org/10.1007/s11432-019-2748-x
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DOI: https://doi.org/10.1007/s11432-019-2748-x