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Abstract

Holistic consideration of the human and the robot is necessary to overcome hurdles in human-robot interaction.

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REFERENCES

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P. Slade, C. Atkeson, J. M. Donelan, H. Houdijk, K. A. Ingraham, M. Kim, K. Kong, K. L. Poggensee, R. Reiner, M. Steinert, J. Zhang, S. H. Collins, On human-in-the-loop optimization of human-robot interaction. Nature 633, 779–788 (2024).
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J. Kluzik, J. Diedrichsen, R. Shadmehr, A. J. Bastian, Reach adaptation: What determines whether we learn an internal model of the tool or adapt the model of our arm? J. Neurophysiol. 100, 1455–1464 (2008).
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J. Lee, M. E. Huber, N. Hogan, Gait entrainment to torque pulses from a hip exoskeleton robot. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 656–667 (2022).
4
E. S. Cross, R. Hortensius, A. Wykowska, From social brains to social robots: Applying neurocognitive insights to human–robot interaction. Philos. Trans. R. Soc. B 374, 20180024 (2019).
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C. Fang, L. Peternel, A. Seth, M. Sartori, K. Mombaur, E. Yoshida, Human modeling in physical human-robot interaction: A brief survey. IEEE Robot. Autom. Lett. 8, 5799–5806 (2023).
6
N. Stergiou, L. M. Decker, Human movement variability, nonlinear dynamics, and pathology: Is there a connection? Hum. Mov. Sci. 30, 869–888 (2011).
7
J. C. Selinger, J. D. Wong, S. N. Simha, J. M. Donelan, How humans initiate energy optimization and converge on their optimal gaits. J. Exp. Biol. 222, jeb198234 (2019).
8
E. Todorov, Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004).
9
E. C. Yu, D. A. Lagnado, The influence of initial beliefs on judgments of probability. Front. Psychol. 3, 381 (2012).
10
H. L. Fernandes, K. P. Kording, In praise of “false” models and rich data. J. Mot. Behav. 42, 343–349 (2010).

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Published In

Science Robotics
Volume 9 | Issue 96
November 2024

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Submission history

Received: 14 March 2024
Accepted: 11 October 2024

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Corresponding author. Email: thiggins@eng.famu.fsu.edu

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References

References

1
P. Slade, C. Atkeson, J. M. Donelan, H. Houdijk, K. A. Ingraham, M. Kim, K. Kong, K. L. Poggensee, R. Reiner, M. Steinert, J. Zhang, S. H. Collins, On human-in-the-loop optimization of human-robot interaction. Nature 633, 779–788 (2024).
2
J. Kluzik, J. Diedrichsen, R. Shadmehr, A. J. Bastian, Reach adaptation: What determines whether we learn an internal model of the tool or adapt the model of our arm? J. Neurophysiol. 100, 1455–1464 (2008).
3
J. Lee, M. E. Huber, N. Hogan, Gait entrainment to torque pulses from a hip exoskeleton robot. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 656–667 (2022).
4
E. S. Cross, R. Hortensius, A. Wykowska, From social brains to social robots: Applying neurocognitive insights to human–robot interaction. Philos. Trans. R. Soc. B 374, 20180024 (2019).
5
C. Fang, L. Peternel, A. Seth, M. Sartori, K. Mombaur, E. Yoshida, Human modeling in physical human-robot interaction: A brief survey. IEEE Robot. Autom. Lett. 8, 5799–5806 (2023).
6
N. Stergiou, L. M. Decker, Human movement variability, nonlinear dynamics, and pathology: Is there a connection? Hum. Mov. Sci. 30, 869–888 (2011).
7
J. C. Selinger, J. D. Wong, S. N. Simha, J. M. Donelan, How humans initiate energy optimization and converge on their optimal gaits. J. Exp. Biol. 222, jeb198234 (2019).
8
E. Todorov, Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004).
9
E. C. Yu, D. A. Lagnado, The influence of initial beliefs on judgments of probability. Front. Psychol. 3, 381 (2012).
10
H. L. Fernandes, K. P. Kording, In praise of “false” models and rich data. J. Mot. Behav. 42, 343–349 (2010).
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