Demir, K. A., Döven, G. & Sezen, B. Industry 5.0 and human-robot co-working. Procedia Comput. Sci. 158, 688â695 (2019).
Farina, D. et al. Toward higher-performance bionic limbs for wider clinical use. Nat. Biomed. Eng. 7, 473â485 (2023).
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.
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).
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.) 653â673 (McGraw-Hill, 2000).
Halilaj, E. et al. Machine learning in human movement biomechanics: best practices, common pitfalls, and new opportunities. J. Biomech. 81, 1â11 (2018).
Alili, A. et al. A novel framework to facilitate user preferred tuning for a robotic knee prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 895â903 (2023).
Franks, P. W. et al. in Proc. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) 700â707 (IEEE, 2020). This study demonstrates the shortcomings of simulation-based optimization of humanârobot interactions.
Diaz, M. A. et al. Human-in-the-loop optimization of wearable robotic devices to improve humanârobot interaction: a systematic review. IEEE Trans. Cybern. 53, 7483â7496 (2022).
Zhang, J. et al. Human-in-the-loop optimization of exoskeleton assistance during walking. Science 356, 1280â1284 (2017). This study highlights the effectiveness of human-in-the-loop optimization for increasing the benefits of an exoskeleton.
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 humanârobot interaction.
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).
Bryan, G. M. et al. Optimized hipâkneeâankle exoskeleton assistance reduces the metabolic cost of walking with worn loads. J. Neuroeng. Rehabil. 18, 161 (2021).
Song, S. & Collins, S. H. Optimizing exoskeleton assistance for faster self-selected walking. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 786â795 (2021).
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.
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).
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).
Slade, P., Kochenderfer, M. J., Delp, S. L. & Collins, S. H. Personalizing exoskeleton assistance while walking in the real world. Nature 610, 277â282 (2022). This study demonstrates a data-driven method for human-in-the-loop optimization and provides an example of optimization under naturalistic conditions.
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).
Lee, U. H. et al. User preference optimization for control of ankle exoskeletons using sample efficient active learning. Sci. Robot. 8, eadg3705 (2023).
Kantharaju, P. et al. Reducing squat physical effort using personalized assistance from an ankle exoskeleton. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 1786â1795 (2022).
Pang, M. et al. Stiffness optimization based on muscle fatigue and muscle synergy for passive waist assistive exoskeleton. Robotic Intell. Autom. 43, 209â224 (2023).
Koginov, G. et al. Human-in-the-loop personalization of a bi-articular wearable robotâs assistance for downhill walking. IEEE Trans. Med. Robot. Bionics 6, 328â339 (2023).
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) 5907â5912 (IEEE, 2017).
Liu, R. et al. Adaptive symmetry reference trajectory generation in shared autonomy for active knee orthosis. IEEE Robot. Autom. Lett. 8, 3118â3125 (2023).
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, 7920â7932 (2023).
Kantharaju, P. et al. Framework for personalizing wearable devices using real-time physiological measures. IEEE Access 11, 81389â81400 (2023).
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) 3431â3436 (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, 2346â2356 (2019).
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.
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, 164â173 (2024).
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).
Felt, W., Selinger, J. C., Donelan, J. M. & Remy, C. D. âBody-in-the-loopâ: 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.
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, 1070â1084 (2019).
Catkin, B. & Patoglu, V. Preference-based human-in-the-loop optimization for perceived realism of haptic rendering. IEEE Trans. Haptics 16, 470â476 (2023).
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.
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, 810â822 (2023).
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, 1674â1680 (2017).
Nuesslein, C. et al. Comparing metabolic cost and muscle activation for knee and back exoskeletons in lifting. IEEE Trans. Med. Robot. Bionics 6, 224â234 (2023).
Kazerooni, H., Racine, J.-L., Huang, L. & Steger, R. in Proc. 2005 IEEE International Conference on Robotics and Automation 4353â4360 (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, 281â288 (1998).
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).
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, 828â835 (2020).
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, 541â557 (2015).
Caputo, J. M. & Collins, S. H. A universal ankleâfoot prosthesis emulator for human locomotion experiments. J. Biomech. Eng. 136, 035002 (2014).
Witte, K. A., Zhang, J., Jackson, R. W. & Collins, S. H. in Proc. 2015 IEEE International Conference on Robotics and Automation (ICRA) 1223â1228 (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).
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).
Sanz-Pena, I., Jeong, H. & Kim, M. Personalized wearable ankle robot using modular additive manufacturing design. IEEE Robot. Autom. Lett. 8, 4935â4942 (2023).
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).
Walsh, C. Human-in-the-loop development of soft wearable robots. Nat. Rev. Mater. 3, 78â80 (2018).
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).
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).
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, 198â208 (2023).
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, 2143â2152 (2021).
Caputo, J. M. et al. Robotic emulation of candidate prosthetic foot designs may enable efficient, evidence-based, and individualized prescriptions. J. Prosthet. Orthot. 34, 202â212 (2022).
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).
Arelekatti, V. N. M. & Winter, A. G. V. in Proc. 2015 IEEE International Conference on Rehabilitation Robotics (ICORR) 350â356 (IEEE, 2015).
Mattson, C. A. & Winter, A. G. Why the developing world needs mechanical design. J. Mech. Des. 138, 070301 (2016).
EikevÃ¥g, S. W., Erichsen, J. F. & Steinert, M. in Proc. The Engineering of Sport 14 1â2 (International Sports Engineering Association, 2022).
Quintero, D., Villarreal, D. J., Lambert, D. J., Kapp, S. & Gregg, R. D. Continuous-phase control of a powered kneeâankle prosthesis: amputee experiments across speeds and inclines. IEEE Trans. Robot. 34, 686â701 (2018).
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, 263â273 (2010).
Varol, H. A., Sup, F. & Goldfarb, M. Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Trans. Biomed. Eng. 57, 542â551 (2009).
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).
Tran, M., Gabert, L., Cempini, M. & Lenzi, T. A lightweight, efficient fully powered knee prosthesis with actively variable transmission. IEEE Robot. Autom. Lett. 4, 1186â1193 (2019).
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).
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).
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).
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).
Flach, P. & Matsubara, E. in Dagstuhl Seminar Proceedings Vol. 7161 1â10 (Schloss DagstuhlâLeibniz-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) 10483â10489 (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).
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, 1682â1690 (2011).
Finley, J. M., Bastian, A. J. & Gottschall, J. S. Learning to be economical: the energy cost of walking tracks motor adaptation. J. Physiol. 591, 1081â1095 (2013).
Nikolaidis, S., Nath, S., Procaccia, A. D. & Srinivasa, S. in Proc. 2017 ACM/IEEE International Conference on Human-Robot Interaction 323â331 (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).
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).
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).
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, 1395â1409 (2022). This study illustrates the potential for humanârobot interaction to improve mobility for individuals with severe impairments.
Antos, S. A., Kording, K. P. & Gordon, K. E. Energy expenditure does not solely explain step lengthâwidth choices during walking. J. Exp. Biol. 225, jeb243104 (2022).
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).
Mombaur, K., Truong, A. & Laumond, J. P. From human to humanoid locomotionâan inverse optimal control approach. Auton. Robots 28, 369â383 (2010).
Tucker, M. et al. in Proc. 2020 IEEE International Conference on Robotics and Automation (ICRA) 2351â2357 (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).
Brunner, C., Fischer, A., Luig, K. & Thies, T. Pairwise support vector machines and their application to large scale problems. J. Mach. Learn. Res. 13, 2279â2292 (2012).
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.) 75â102 (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).
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, 944â952 (2021).
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) 22â28 (IEEE, 2016).
Kim, M. et al. Human-in-the-loop Bayesian optimization of wearable device parameters. PLoS One 12, e0184054 (2017).
Kim, M. et al. in Proc. 2019 International Conference on Robotics and Automation (ICRA) 9173â9179 (IEEE, 2019).
Denning, P. J. Working sets past and present. IEEE Trans. Softw. Eng. 1, 64â84 (1980).
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).
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, 3055â3065 (2011).
Macready, W. G. & Wolpert, D. H. Bandit problems and the exploration/exploitation tradeoff. IEEE Trans. Evol. Comput. 2, 2â22 (1998).
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).
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).
Waldherr, S., Romero, R. & Thrun, S. A gesture based interface for human-robot interaction. Auton. Robots 9, 151â173 (2000).
Landi, C. T., Ferraguti, F., Fantuzzi, C. & Secchi, C. in Proc. 2018 IEEE International Conference on Robotics and Automation (ICRA) 3279â3284 (IEEE, 2018).
Xiao, X. et al. APPL: adaptive planner parameter learning. Robot. Auton. Syst. 154, 104132 (2022).
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, 2087â2096 (2019).
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, 1848â1855 (2019).
Abram, S. J. et al. General variability leads to specific adaptation toward optimal movement policies. Curr. Biol. 32, 2222â2232 (2022).
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 splitâbelt treadmill walking. J. Physiol. 601, 3921â3944 (2023).
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) 771â776 (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, 28517â28529 (2022).
Kim, M. et al. Visual guidance can help with the use of a robotic exoskeleton during human walking. Sci. Rep. 12, 3881 (2022).
Madden, J. D. Mobile robots: motor challenges and materials solutions. Science 318, 1094â1097 (2007).
Burden, S. A., Libby, T., Jayaram, K., Sponberg, S. & Donelan, J. Why animals can outrun robots. Sci. Robot. 9, eadi9754 (2024).
Riener, R., Rabezzana, L. & Zimmermann, Y. D. Do robots outperform humans in human-centered domains? Front. Robot. AI 10, 1223946 (2023).
Collins, S. H., Wiggin, M. B. & Sawicki, G. S. Reducing the energy cost of human walking using an unpowered exoskeleton. Nature 522, 212â215 (2015).
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, 1549â1557 (2017).
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).
