On human-in-the-loop optimization of human–robot interaction – Nature

    0
    On human-in-the-loop optimization of human–robot interaction – Nature


  • Demir, K. A., Döven, G. & Sezen, B. Industry 5.0 and human-robot co-working. Procedia Comput. Sci. 158, 688–695 (2019).

    Article 

    Google Scholar
     

  • Farina, D. et al. Toward higher-performance bionic limbs for wider clinical use. Nat. Biomed. Eng. 7, 473–485 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Sawicki, G. S., Beck, O. N., Kang, I. & Young, A. J. The exoskeleton expansion: improving walking and running economy. J. Neuroeng. Rehabil. 17, 25 (2020). This review presents a timeline of lower-limb exoskeleton development and performance enhancements.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Crea, S. et al. Occupational exoskeletons: a roadmap toward large-scale adoption. Methodology and challenges of bringing exoskeletons to workplaces. Wearable Technol. 2, e11 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Uchida, T. K. & Delp, S. L. Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation (MIT Press, 2021).

  • Ghez, C. & Krakauer, J. in Principles of Neural Science 4th edn (eds Kandel, E. R., Schwartz, J. H. & Jessell, T. M.) 653–673 (McGraw-Hill, 2000).

  • Halilaj, E. et al. Machine learning in human movement biomechanics: best practices, common pitfalls, and new opportunities. J. Biomech. 81, 1–11 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alili, A. et al. A novel framework to facilitate user preferred tuning for a robotic knee prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 895–903 (2023).

    Article 

    Google Scholar
     

  • Franks, P. W. et al. in Proc. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) 700–707 (IEEE, 2020). This study demonstrates the shortcomings of simulation-based optimization of human–robot interactions.

  • Diaz, M. A. et al. Human-in-the-loop optimization of wearable robotic devices to improve human–robot interaction: a systematic review. IEEE Trans. Cybern. 53, 7483–7496 (2022).

    Article 

    Google Scholar
     

  • Zhang, J. et al. Human-in-the-loop optimization of exoskeleton assistance during walking. Science 356, 1280–1284 (2017). This study highlights the effectiveness of human-in-the-loop optimization for increasing the benefits of an exoskeleton.

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Poggensee, K. L. & Collins, S. H. How adaptation, training, and customization contribute to benefits from exoskeleton assistance. Sci. Robot. 6, eabf1078 (2021). This study highlights the importance of human adaptation in achieving effective human–robot interaction.

    Article 
    PubMed 

    Google Scholar
     

  • Witte, K. A., Fiers, P., Sheets-Singer, A. L. & Collins, S. H. Improving the energy economy of human running with powered and unpowered ankle exoskeleton assistance. Sci. Robot. 5, eaay9108 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Bryan, G. M. et al. Optimized hip–knee–ankle exoskeleton assistance reduces the metabolic cost of walking with worn loads. J. Neuroeng. Rehabil. 18, 161 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Song, S. & Collins, S. H. Optimizing exoskeleton assistance for faster self-selected walking. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 786–795 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ding, Y., Kim, M., Kuindersma, S. & Walsh, C. J. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Sci. Robot. 3, eaar5438 (2018). This study illustrates the use of Bayesian optimization for human-in-the-loop optimization.

    Article 
    PubMed 

    Google Scholar
     

  • Kim, J. et al. Reducing the energy cost of walking with low assistance levels through optimized hip flexion assistance from a soft exosuit. Sci. Rep. 12, 11004 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Haufe, F., Wolf, P. & Riener, R. Human-in-the-loop optimization of a multi-joint wearable robot for movement assistance. Proc. Autom. Med. Eng. 1, 023 (2020).


    Google Scholar
     

  • Slade, P., Kochenderfer, M. J., Delp, S. L. & Collins, S. H. Personalizing exoskeleton assistance while walking in the real world. Nature 610, 277–282 (2022). This study demonstrates a data-driven method for human-in-the-loop optimization and provides an example of optimization under naturalistic conditions.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ingraham, K. A., Remy, C. D. & Rouse, E. J. The role of user preference in the customized control of robotic exoskeletons. Sci. Robot. 7, eabj3487 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lee, U. H. et al. User preference optimization for control of ankle exoskeletons using sample efficient active learning. Sci. Robot. 8, eadg3705 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Kantharaju, P. et al. Reducing squat physical effort using personalized assistance from an ankle exoskeleton. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 1786–1795 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Pang, M. et al. Stiffness optimization based on muscle fatigue and muscle synergy for passive waist assistive exoskeleton. Robotic Intell. Autom. 43, 209–224 (2023).

    Article 

    Google Scholar
     

  • Koginov, G. et al. Human-in-the-loop personalization of a bi-articular wearable robot’s assistance for downhill walking. IEEE Trans. Med. Robot. Bionics 6, 328–339 (2023).

    Article 

    Google Scholar
     

  • Hamaya, M., Matsubara, T., Noda, T., Teramae, T. & Morimoto, J. Learning task-parameterized assistive strategies for exoskeleton robots by multi-task reinforcement learning. In IEEE International Conference on Robotics and Automation (ICRA) 5907–5912 (IEEE, 2017).

  • Liu, R. et al. Adaptive symmetry reference trajectory generation in shared autonomy for active knee orthosis. IEEE Robot. Autom. Lett. 8, 3118–3125 (2023).

    Article 

    Google Scholar
     

  • Li, Z., Li, Q., Huang, P., Xia, H. & Li, G. Human-in-the-loop adaptive control of a soft exo-suit with actuator dynamics and ankle impedance adaptation. IEEE Trans. Cybern. 53, 7920–7932 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Kantharaju, P. et al. Framework for personalizing wearable devices using real-time physiological measures. IEEE Access 11, 81389–81400 (2023).

    Article 

    Google Scholar
     

  • Wen, T. C., Jacobson, M., Zhou, X., Chung, H. J. & Kim, M. in Proc. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 3431–3436 (IEEE, 2020).

  • Wen, Y., Si, J., Brandt, A., Gao, X. & Huang, H. H. Online reinforcement learning control for the personalization of a robotic knee prosthesis. IEEE Trans. Cybern. 50, 2346–2356 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Tankink, T., Carloni, R. & Hijmans, J. M. & Houdijk, H. Human-in-the-loop optimization of rocker shoes via different cost functions during walking. J. Biomech. 166, 112028 (2024). This study provides an example of human-in-the-loop optimization of a non-robotic device.

    Article 
    PubMed 

    Google Scholar
     

  • Tankink, T., Houdijk, H. & Hijmans, J. M. Human-in-the-loop optimized rocker profile of running shoes to enhance ankle work and running economy. Eur. J. Sport Sci. 24, 164–173 (2024).

    Article 
    PubMed Central 

    Google Scholar
     

  • Huang, G., Lin, S. & Xie, L. Human-in-the-loop optimization of knee-joint biomechanical energy harvester to maximize power generation with minimal user effort. Energy Convers. Manage. 283, 116913 (2023).

    Article 

    Google Scholar
     

  • Felt, W., Selinger, J. C., Donelan, J. M. & Remy, C. D. “Body-in-the-loop”: optimizing device parameters using measures of instantaneous energetic cost. PLoS One 10, e0135342 (2015). This study provides an example of an early, gradient-based approach to human-in-the-loop optimization.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Garcia-Rosas, R., Tan, Y., Oetomo, D., Manzie, C. & Choong, P. Personalized online adaptation of kinematic synergies for human-prosthesis interfaces. IEEE Tran. Cybern. 51, 1070–1084 (2019).

    Article 

    Google Scholar
     

  • Catkin, B. & Patoglu, V. Preference-based human-in-the-loop optimization for perceived realism of haptic rendering. IEEE Trans. Haptics 16, 470–476 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Fauvel, T. & Chalk, M. Human-in-the-loop optimization of visual prosthetic stimulation. J. Neural Eng. 19, 036038 (2022). This study provides an example of user preference as an optimization objective, in this case applied to a retinal prosthesis.

    Article 
    ADS 

    Google Scholar
     

  • Sánchez, N. et al. Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls. Neurorehabil. Neural Repair 37, 810–822 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lamers, E. P., Yang, A. J. & Zelik, K. E. Feasibility of a biomechanically-assistive garment to reduce low back loading during leaning and lifting. IEEE Trans. Biomed. Eng. 65, 1674–1680 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nuesslein, C. et al. Comparing metabolic cost and muscle activation for knee and back exoskeletons in lifting. IEEE Trans. Med. Robot. Bionics 6, 224–234 (2023).

    Article 

    Google Scholar
     

  • Kazerooni, H., Racine, J.-L., Huang, L. & Steger, R. in Proc. 2005 IEEE International Conference on Robotics and Automation 4353–4360 (IEEE, 2005). This study describes an early exoskeleton that did not improve user performance despite extensive investment, illustrating the risks of a traditional development approach.

  • Garcia, M., Chatterjee, A., Ruina, A. & Coleman, M. The simplest walking model: stability, complexity, and scaling. J. Biomech. Eng. 120, 281–288 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dembia, C. L., Silder, A., Uchida, T. K., Hicks, J. L. & Delp, S. L. Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLoS One 12, e0180320 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Siviy, C. et al. Offline assistance optimization of a soft exosuit for augmenting ankle power of stroke survivors during walking. IEEE Robot. Autom. Lett. 5, 828–835 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jackson, R. W. & Collins, S. H. An experimental comparison of the relative benefits of work and torque assistance in ankle exoskeletons. J. Appl. Physiol. 119, 541–557 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Caputo, J. M. & Collins, S. H. A universal ankle–foot prosthesis emulator for human locomotion experiments. J. Biomech. Eng. 136, 035002 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Witte, K. A., Zhang, J., Jackson, R. W. & Collins, S. H. in Proc. 2015 IEEE International Conference on Robotics and Automation (ICRA) 1223–1228 (IEEE, 2015).

  • Anderson, A. et al. A robotic emulator for the systematic exploration of transtibial biarticular prosthesis designs. Preprint at https://doi.org/10.36227/techrxiv.24417310.v1 (2023).

  • Portnova, A. A., Mukherjee, G., Peters, K. M., Yamane, A. & Steele, K. M. Design of a 3D-printed, open-source wrist-driven orthosis for individuals with spinal cord injury. PLoS One 13, e0193106 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Severin, A. C. et al. Case report: adjusting seat and backrest angle improves performance in an elite paralympic rower. Front. Sports Act. Living 3, 625656 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sanz-Pena, I., Jeong, H. & Kim, M. Personalized wearable ankle robot using modular additive manufacturing design. IEEE Robot. Autom. Lett. 8, 4935–4942 (2023).

    Article 

    Google Scholar
     

  • Sloot, L. H. et al. Effects of a soft robotic exosuit on the quality and speed of overground walking depends on walking ability after stroke. J. Neuroeng. Rehabil. 20, 113 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walsh, C. Human-in-the-loop development of soft wearable robots. Nat. Rev. Mater. 3, 78–80 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Xu, L. et al. Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization. Front. Bioeng. Biotechnol. 11, 1006326 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kong, H. M. A Personalized Quasi-passive Ankle Exoskeleton Using Human-in-the loop Optimization Approaches Doctoral dissertation, KTH Royal Institute of Technology (2023).

  • Hybart, R., Villancio-Wolter, K. S. & Ferris, D. P. Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors. PeerJ 11, e15775 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kinsey, H., Upton, E. & Young, A. Towards meaningful community ambulation in individuals post stroke through use of a smart hip exoskeleton: a preliminary investigation. Assist. Technol. 36, 198–208 (2023).


    Google Scholar
     

  • Fang, Y., Orekhov, G. & Lerner, Z. Improving the energy cost of incline walking and stair ascent with ankle exoskeleton assistance in cerebral palsy. IEEE Trans. Biomed. Eng. 69, 2143–2152 (2021).

    Article 

    Google Scholar
     

  • Caputo, J. M. et al. Robotic emulation of candidate prosthetic foot designs may enable efficient, evidence-based, and individualized prescriptions. J. Prosthet. Orthot. 34, 202–212 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Welker, C. G., Voloshina, A. S., Chiu, V. L. & Collins, S. H. Shortcomings of human-in-the-loop optimization of an ankle-foot prosthesis emulator: a case series. R. Soc. Open Sci. 8, 202020 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Arelekatti, V. N. M. & Winter, A. G. V. in Proc. 2015 IEEE International Conference on Rehabilitation Robotics (ICORR) 350–356 (IEEE, 2015).

  • Mattson, C. A. & Winter, A. G. Why the developing world needs mechanical design. J. Mech. Des. 138, 070301 (2016).

    Article 

    Google Scholar
     

  • Eikevåg, S. W., Erichsen, J. F. & Steinert, M. in Proc. The Engineering of Sport 14 1–2 (International Sports Engineering Association, 2022).

  • Quintero, D., Villarreal, D. J., Lambert, D. J., Kapp, S. & Gregg, R. D. Continuous-phase control of a powered knee–ankle prosthesis: amputee experiments across speeds and inclines. IEEE Trans. Robot. 34, 686–701 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Geyer, H. & Herr, H. A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 263–273 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Varol, H. A., Sup, F. & Goldfarb, M. Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Trans. Biomed. Eng. 57, 542–551 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Simon, A. M. et al. Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes. PLoS One 9, e99387 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tran, M., Gabert, L., Cempini, M. & Lenzi, T. A lightweight, efficient fully powered knee prosthesis with actively variable transmission. IEEE Robot. Autom. Lett. 4, 1186–1193 (2019).

    Article 

    Google Scholar
     

  • Song, Y., Romero, A., Müller, M., Koltun, V. & Scaramuzza, D. Reaching the limit in autonomous racing: optimal control versus reinforcement learning. Sci. Robot. 8, eadg1462 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Slade, P., Kochenderfer, M. J., Delp, S. L. & Collins, S. H. Sensing leg movement enhances wearable monitoring of energy expenditure. Nat. Commun. 12, 4312 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Revi, D. A., Alvarez, A. M., Walsh, C. J., De Rossi, S. M. & Awad, L. N. Indirect measurement of anterior-posterior ground reaction forces using a minimal set of wearable inertial sensors: from healthy to hemiparetic walking. J. Neuroeng. Rehabil. 17, 82 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramadurai, S., Jeong, H. & Kim, M. Predicting the metabolic cost of exoskeleton-assisted squatting using foot pressure features and machine learning. Front. Robot. AI 10, 1166248 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Flach, P. & Matsubara, E. in Dagstuhl Seminar Proceedings Vol. 7161 1–10 (Schloss Dagstuhl–Leibniz-Zentrum für Informatik, 2008).

  • Wang, W., Raitor, M., Collins, S., Liu, C. K. & Kennedy, M. in Proc. 2023 IEEE International Conference on Robotics and Automation (ICRA) 10483–10489 (IEEE, 2023).

  • Eveld, M. E., King, S. T., Vailati, L. G., Zelik, K. E. & Goldfarb, M. On the basis for stumble recovery strategy selection in healthy adults. J. Biomech. Eng. 143, 071003 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chasnov, B. J., Ratliff, L. J. & Burden, S. A. Human adaptation to adaptive machines converges to game-theoretic equilibria. Preprint at https://arxiv.org/abs/2305.01124 (2023).

  • Snaterse, M., Ton, R., Kuo, A. D. & Donelan, J. M. Distinct fast and slow processes contribute to the selection of preferred step frequency during human walking. J. Appl. Physiol. 110, 1682–1690 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Finley, J. M., Bastian, A. J. & Gottschall, J. S. Learning to be economical: the energy cost of walking tracks motor adaptation. J. Physiol. 591, 1081–1095 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nikolaidis, S., Nath, S., Procaccia, A. D. & Srinivasa, S. in Proc. 2017 ACM/IEEE International Conference on Human-Robot Interaction 323–331 (IEEE, 2017).

  • Medrano, R. L., Thomas, G. C., Margolin, D. & Rouse, E. J. The economic value of augmentative exoskeletons and their assistance. Commun. Eng. 2, 43 (2023).

    Article 
    PubMed Central 

    Google Scholar
     

  • Brown, G. L., Seethapathi, N. & Srinivasan, M. A unified energy-optimality criterion predicts human navigation paths and speeds. Proc. Natl Acad. Sci. 118, e2020327118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • IJmker, T., Lamoth, C. J., Houdijk, H., van der Woude, L. H. & Beek, P. J. Postural threat during walking: effects on energy cost and accompanying gait changes. J. Neuroeng. Rehabil. 11, 71 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Park, K. W., Choi, J. & Kong, K. Iterative learning of human behavior for adaptive gait pattern adjustment of a powered exoskeleton. IEEE Trans. Robot. 38, 1395–1409 (2022). This study illustrates the potential for human–robot interaction to improve mobility for individuals with severe impairments.

    Article 

    Google Scholar
     

  • Antos, S. A., Kording, K. P. & Gordon, K. E. Energy expenditure does not solely explain step length–width choices during walking. J. Exp. Biol. 225, jeb243104 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McDonald, K. A., Cusumano, J. P., Hieronymi, A. & Rubenson, J. Humans trade off whole-body energy cost to avoid overburdening muscles while walking. Proc. R. Soc. B 289, 20221189 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mombaur, K., Truong, A. & Laumond, J. P. From human to humanoid locomotion—an inverse optimal control approach. Auton. Robots 28, 369–383 (2010).

    Article 

    Google Scholar
     

  • Tucker, M. et al. in Proc. 2020 IEEE International Conference on Robotics and Automation (ICRA) 2351–2357 (IEEE, 2020).

  • Ingraham, K. A., Tucker, M., Ame, A. D., Rouse, E. J. & Shepherd, M. K. Leveraging user preference in the design and evaluation of lower-limb exoskeletons and prostheses. Curr. Opin. Biomed. Eng. 28, 100487 (2023).

    Article 

    Google Scholar
     

  • Brunner, C., Fischer, A., Luig, K. & Thies, T. Pairwise support vector machines and their application to large scale problems. J. Mach. Learn. Res. 13, 2279–2292 (2012).

    MathSciNet 

    Google Scholar
     

  • Astudillo, R. et al. in Proc. ICML 2023 Workshop The Many Facets of Preference-Based Learning (ICML, 2023).

  • Hansen, N. in Towards a New Evolutionary Computation. Studies in Fuzziness and Soft Computing, Vol. 192 (eds Lozano, J. A., Larrañaga, P., Inza, I. & Bengoetxea, E.) 75–102 (Springer, 2006).

  • Kochenderfer, M. J. & Wheeler, T. A. Algorithms for Optimization (MIT Press, 2019).

  • Lakmazaheri, A. et al. Optimizing exoskeleton assistance to improve walking speed and energy economy for older adults. J. Neuroeng. Rehabil. 21, 1 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Han, H. et al. Selection of muscle-activity-based cost function in human-in-the-loop optimization of multi-gait ankle exoskeleton assistance. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 944–952 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Kutulakos, Z. & Slade, P. Simulating human-in-the-loop optimization of exoskeleton assistance to compare optimization algorithm performance. Preprint at bioRxiv https://doi.org/10.1101/2024.04.05.587982 (2024).

  • Antonova, R., Rai, A. & Atkeson, C. G. in Proc. 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) 22–28 (IEEE, 2016).

  • Kim, M. et al. Human-in-the-loop Bayesian optimization of wearable device parameters. PLoS One 12, e0184054 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, M. et al. in Proc. 2019 International Conference on Robotics and Automation (ICRA) 9173–9179 (IEEE, 2019).

  • Denning, P. J. Working sets past and present. IEEE Trans. Softw. Eng. 1, 64–84 (1980).

    Article 
    ADS 

    Google Scholar
     

  • Franks, P. W. et al. Comparing optimized exoskeleton assistance of the hip, knee, and ankle in single and multi-joint configurations. Wearable Technol. 2, e16 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vasudevan, E. V., Torres-Oviedo, G., Morton, S. M., Yang, J. F. & Bastian, A. J. Younger is not always better: development of locomotor adaptation from childhood to adulthood. J. Neurosci. 31, 3055–3065 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Macready, W. G. & Wolpert, D. H. Bandit problems and the exploration/exploitation tradeoff. IEEE Trans. Evol. Comput. 2, 2–22 (1998).

    Article 

    Google Scholar
     

  • McAllister, M. J., Blair, R. L., Donelan, J. M. & Selinger, J. C. Energy optimization during walking involves implicit processing. J. Exp. Biol. 224, jeb242655 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Hybart, R. & Ferris, D. Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control. PLoS One 18, e0294241 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Waldherr, S., Romero, R. & Thrun, S. A gesture based interface for human-robot interaction. Auton. Robots 9, 151–173 (2000).

    Article 

    Google Scholar
     

  • Landi, C. T., Ferraguti, F., Fantuzzi, C. & Secchi, C. in Proc. 2018 IEEE International Conference on Robotics and Automation (ICRA) 3279–3284 (IEEE, 2018).

  • Xiao, X. et al. APPL: adaptive planner parameter learning. Robot. Auton. Syst. 154, 104132 (2022).

    Article 

    Google Scholar
     

  • Kristoffersen, M. B., Franzke, A. W., van der Sluis, C. K., Murgia, A. & Bongers, R. M. The effect of feedback during training sessions on learning pattern-recognition-based prosthesis control. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 2087–2096 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Wong, J. D., Selinger, J. C. & Donelan, J. C. Is natural variability in gait sufficient to initiate spontaneous energy optimization in human walking? J. Neurophysiol. 121, 1848–1855 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Abram, S. J. et al. General variability leads to specific adaptation toward optimal movement policies. Curr. Biol. 32, 2222–2232 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Song, S., Haynes, C. A. & Bradford, J. C. Human cortical, muscular, and kinematic gait adaptation with novel use of an ankle exoskeleton. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-2675191/v1 (2023).

  • Jacobsen, N. A. & Ferris, D. P. Electrocortical activity correlated with locomotor adaptation during split‐belt treadmill walking. J. Physiol. 601, 3921–3944 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mu, T., Goel, K. & Brunskill, E. in Proc. 31st Conference on Neural Information Processing Systems (NIPS 2017) (Curran Associates, 2017).

  • Ghonasgi, K. et al. in Proc. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 771–776 (IEEE, 2021).

  • Byeon, S., Choi, J., Zhang, Y. & Hwang, I. Stochastic-skill-level-based shared control for human training in urban air mobility scenario. ACM Trans. Hum.-Robot Interact. (in the press).

  • Srivastava, M., Biyik, E., Mirchandani, S., Goodman, N. & Sadigh, D. Assistive teaching of motor control tasks to humans. Adv. Neural Inf. Process. Syst. 35, 28517–28529 (2022).


    Google Scholar
     

  • Kim, M. et al. Visual guidance can help with the use of a robotic exoskeleton during human walking. Sci. Rep. 12, 3881 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Madden, J. D. Mobile robots: motor challenges and materials solutions. Science 318, 1094–1097 (2007).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Burden, S. A., Libby, T., Jayaram, K., Sponberg, S. & Donelan, J. Why animals can outrun robots. Sci. Robot. 9, eadi9754 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Riener, R., Rabezzana, L. & Zimmermann, Y. D. Do robots outperform humans in human-centered domains? Front. Robot. AI 10, 1223946 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Collins, S. H., Wiggin, M. B. & Sawicki, G. S. Reducing the energy cost of human walking using an unpowered exoskeleton. Nature 522, 212–215 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, H. J. et al. A wearable hip assist robot can improve gait function and cardiopulmonary metabolic efficiency in elderly adults. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1549–1557 (2017).

    ADS 
    PubMed 

    Google Scholar
     

  • Mooney, L. M., Rouse, E. J. & Herr, H. M. Autonomous exoskeleton reduces metabolic cost of human walking during load carriage. J. Neuroeng. Rehabil. 11, 80 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     



  • Source link