Robot-Assisted Upper Limb Rehabilitation Using Imitation Learning
DOI:
https://doi.org/10.18196/jrc.v6i1.23927Keywords:
Dynamic Movement Primitives, Learning by Demonstration, Model Adaptive Control, Personalized Therapy.Abstract
Robotic rehabilitation offers an innovative approach to enhance motor function recovery in patients with upper-limb impairment. However, the primary challenge lies in the development of adaptive and personalized therapies to meet the unique needs of patients. In response to this challenge, this paper presents a Rehabilitation Learning from Demonstration (RLfD) framework, which integrates Dynamic Movement Primitives (DMP) for learning and generalizing movements, and a Model Reference Adaptive Controller (MRAC) for real-time adaptive control. This combination enables a two-link manipulator to accurately replicate and adapt therapist demonstrations specifically designed for upper-limb rehabilitation. Unlike conventional task-specific controllers, which are limited by poor adaptability, minimal feedback, and lack of generalization, our system dynamically adjusts robotic assistance in real time based on the subject’s tracking error to optimize therapy outcomes. The objective is to minimize assistance while maximizing patient participation in the rehabilitation process. To facilitate this, the framework employs visual tracking technology to capture therapist demonstrations accurately. Once captured, the DMP component of the framework learns from these movements and generalizes them to new goals, while maintaining the original motion patterns. Our evaluations with a simulated two-link manipulator demonstrated the framework’s precise trajectory tracking, robust generalization, and adaptability to disturbances mimicking patient impairments. These tests confirmed the system’s ability to follow complex trajectories and adapt to dynamic patient motor functions. The promising results from these evaluations highlight our approach’s potential to significantly enhance adaptability and generalization in variable patient conditions, marking a substantial improvement over conventional systems.References
A. Demeco, L. Zola, A. Frizziero, C. Martini, A. Palumbo, R. Foresti, G. Buccino, and C. Costantino, “Immersive virtual reality in post-stroke rehabilitation: A systematic review,” Sensors, vol. 23, no. 3, 2023, doi: 10.3390/s23031712.
N. Marotta, A. Ammendolia, C. Marinaro, A. Demeco, L. Moggio, and C. Costantino, “International classification of functioning, disability and health (icf) and correlation between disability and finance assets in chronic stroke patients,” Acta Bio Medica: Atenei Parmensis, vol. 91, no. 3, 2020, doi: 10.23750/abm.v91i3.8968.
G. Collaborators and Feigin, “Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the global burden of disease study 2019,” The Lancet Neurology, vol. 20, no. 10, pp. 795–820, 2021, doi: 10.1016/S1474-4422(21)00252-0.
A. Demeco, R. Foresti, A. Frizziero, N. Daracchi, F. Renzi, M. Rovellini, A. Salerno, C. Martini, L. Pelizzari, and C. Costantino, “The upper limb orthosis in the rehabilitation of stroke patients: The role of 3d printing,” Bioengineering, vol. 10, no. 11, 2023, doi: 10.3390/bioengineering10111256.
C. M. Stinear, C. E. Lang, S. Zeiler, and W. D. Byblow, “Advances and challenges in stroke rehabilitation,” The Lancet Neurology, vol. 19, no. 4, pp. 348–360, 2020, doi: 10.1016/S1474-4422(19)30415-6.
K. S. Hayward, S. F. Kramer, E. J. Dalton, G. R. Hughes, A. Brodtmann, L. Churilov, G. Cloud, D. Corbett, L. Jolliffe, T. Kaffenberger, V. Rethnam, V. Thijs, N. Ward, N. Lannin, and J. Bernhardt, “Timing and dose of upper limb motor intervention after stroke: A systematic review,” Stroke, vol. 52, no. 11, pp. 3706–3717, 2021, doi: 10.1161/STROKEAHA.121.034348.
R. Bertani, C. Melegari, M. C. De Cola, A. Bramanti, P. Bramanti, and R. S. Calabrò, “Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis,” Neurological Sciences, vol. 38, pp. 1561–1569, 2017, doi: 10.1007/s10072-017-2995-5.
L. A. Simpson, K. S. Hayward, M. McPeake, T. S. Field, and J. J. Eng, “Challenges of estimating accurate prevalence of arm weakness early after stroke,” Neurorehabilitation and Neural Repair, vol. 35, no. 10, pp. 871– 879, 2021, doi: 10.1177/15459683211028240.
A. G. Dogan, “The effect of occupational therapy on upper extremity ˘ function and activities of daily living in hemiplegic patients,” Journal of Medicine and Palliative Care, vol. 4, pp. 350–354, 2023, doi: 10.47582/jompac.1327960.
B. Zhang, K.-P. Wong, and J. Qin, “Effects of virtual reality on the limb motor function, balance, gait, and daily function of patients with stroke: Systematic review,” Medicina, vol. 59, no. 4, 2023.
J. Huang, J.-R. Ji, C. Liang, Y.-Z. Zhang, H.-C. Sun, Y.-H. Yan, and X.-B. Xing, “Effects of physical therapy-based rehabilitation on recovery of upper limb motor function after stroke in adults: a systematic review and meta-analysis of randomized controlled trials,” Annals of Palliative Medicine, vol. 11, no. 2, pp. 521–531, 2022, doi: 10.21037/apm-21-3710.
M. Franceschini et al., “Upper limb robot-assisted rehabilitation versus physical therapy on subacute stroke patients: A follow-up study,” Journal of Bodywork and Movement Therapies, vol. 24, no. 1, pp. 194–198, 2020, doi: 10.1016/J.JBMT.2019.03.016.
M. Coscia, M. J. Wessel, U. Chaudary, J. d. R. Millán, S. Micera, A. Guggisberg, P. Vuadens, J. Donoghue, N. Birbaumer, and F. C. Hummel, “Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke,” Brain, vol. 142, no. 8, pp. 2182– 2197, 2019, doi: 10.1093/brain/awz181.
J. Bernhardt et al., D. Corbett, and S. C. Cramer, “A stroke recovery trial development framework: Consensus-based core recommendations from the second stroke recovery and rehabilitation roundtable,” International Journal of Stroke, vol. 14, no. 8, pp. 792–802, 2019, doi: 10.1177/1747493019879657.
H. R. E. Reem M. Al-Whaibi, Maher S. Al-Jadid and W. M. Badawy, “Effectiveness of virtual reality-based rehabilitation versus conventional therapy on upper limb motor function of chronic stroke patients: a systematic review and meta-analysis of randomized controlled trials,” Physiotherapy Theory and Practice, vol. 38, no. 13, pp. 2402–2416, 2022, doi: 10.1080/09593985.2021.1941458.
A. N. Malik, H. Tariq, A. Afridi, and F. A. Rathore, “Technological advancements in stroke rehabilitation,” Journal of Pakistan Medical Association, vol. 72, no. 8, pp. 1672–1674, 2022, doi: 10.47391/JPMA.22- 90.
M. Kyrarini, F. Lygerakis, A. Rajavenkatanarayanan, C. Sevastopoulos, H. R. Nambiappan, K. K. Chaitanya, A. R. Babu, J. Mathew, and F. Makedon, “A survey of robots in healthcare,” Technologies, vol. 9, no. 1, 2021, doi: 10.3390/TECHNOLOGIES9010008.
M. Germanotta, L. Cortellini, S. Insalaco, and I. Aprile, “Effects of upper limb robot-assisted rehabilitation compared with conventional therapy in patients with stroke: Preliminary results on a daily task assessed using motion analysis,” Sensors, vol. 23, no. 6, 2023, doi: https://doi.org/10.3390/s23063089.
S. Giovannini, C. Iacovelli, F. Brau, C. Loreti, A. Fusco, P. Caliandro, L. Biscotti, L. Padua, R. Bernabei, and L. Castelli, “Robotic-assisted rehabilitation for balance and gait in stroke patients (roar-s): study protocol for a preliminary randomized controlled trial,” Trials, vol. 23, 2022, doi: 10.1186/s13063-022-06812-w.
S. B. Reis, W. M. Bernardo, C. A. Oshiro, H. I. Krebs, and A. B. Conforto, “Effects of robotic therapy associated with noninvasive brain stimulation on upper-limb rehabilitation after stroke: Systematic review and metaanalysis of randomized clinical trials,” Neurorehabilitation and Neural Repair, vol. 35, no. 3, pp. 256–266, 2021, doi: 10.1177/1545968321989353.
I. Aprile, G. Guardati, V. Cipollini, D. Papadopoulou, A. Mastrorosa, L. Castelli, S. Monteleone, A. Redolfi, S. Galeri, and M. Germanotta, “Robotic rehabilitation: An opportunity to improve cognitive functions in subjects with stroke. an explorative study,” Frontiers in Neurology, vol. 11, 2020, doi: 10.1177/1545968321989353.
R. Iandolo, F. Marini, M. Semprini, M. Laffranchi, M. Mugnosso, A. Cherif, L. De Michieli, M. Chiappalone, and J. Zenzeri, “Perspectives and challenges in robotic neurorehabilitation,” Applied Sciences, vol. 9, no. 15, 2019, doi: 10.3390/APP9153183.
P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, and S. Leonhardt, “A survey on robotic devices for upper limb rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 3, 2014, doi: 10.1186/1743-0003-11-3.
J. Narayan, B. Kalita, and S. K. Dwivedy, “Development of robot-based upper limb devices for rehabilitation purposes: a systematic review,” Augmented Human Research, vol. 6, no. 4, 2021, doi: 10.1007/s41133- 020-00043-x.
S. Bhujel and S. Hasan, “A comparative study of end-effector and exoskeleton type rehabilitation robots in human upper extremity rehabilitation,” Human-Intelligent Systems Integration, vol. 5, pp. 11–42, 2023, doi: 10.1007/s42454-023-00048-y.
Y. Shen, P. W. Ferguson, and J. Rosen, “Upper limb exoskeleton systems: Overview,” Journal of Robotics Research, vol. 45, no. 2, pp. 1–22, 2020, doi: 10.1007/s42454-023-00048-y.
J. F. Mullen, J. Mosier, S. Chakrabarti, A. Chen, T. White, and D. P. Losey, “Communicating inferred goals with passive augmented reality and active haptic feedback,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8522–8529, 2021, doi: 10.1109/LRA.2021.3111055.
J. Zheng, P. Shi, M. Fan, S. Liang, S. Li, and H. Yu, “Effects of passive and active training modes of upper-limb rehabilitation robot on cortical activation: a functional near-infrared spectroscopy study,” NeuroReport, vol. 32, no. 6, pp. 479–488, 2021, doi: 10.1097/WNR.0000000000001615.
Z. Zhao, J. Xiao, H. Jia, H. Zhang, and L. Hao, “Prescribed performance control for the upper-limb exoskeleton system in passive rehabilitation training tasks,” Applied Sciences, vol. 11, no. 21, 2021, doi: 10.3390/app112110174.
A. Nasr, C. R. Dickerson, and J. McPhee, “Experimental study of fully passive, fully active, and active–passive upper-limb exoskeleton efficiency: An assessment of lifting tasks,” Sensors, vol. 24, no. 1, 2024, doi: 10.3390/s24010063.
N. Norouzi-Gheidari, P. S. Archambault, K. Monte-Silva, D. Kairy, H. Sveistrup, M. Trivino, M. F. Levin, and M.-H. Milot, “Feasibility and preliminary efficacy of a combined virtual reality, robotics and electrical stimulation intervention in upper extremity stroke rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 1, 2021, doi: 10.1186/s12984-021-00851-1.
F. Zanatta, A. Giardini, A. Pierobon, M. D’Addario, and P. Steca, “A systematic review on the usability of robotic and virtual reality devices in neuromotor rehabilitation: patients’ and healthcare professionals’ perspective,” BMC Health Services Research, vol. 22, no. 1, 2022, doi: 10.1186/s12913-022-07821-w.
W. E. Clark, M. Sivan, and R. J. O’Connor, “Evaluating the use of robotic and virtual reality rehabilitation technologies to improve function in stroke survivors: A narrative review,” Journal of Rehabilitation and Assistive Technologies Engineering, vol. 6, 2019, doi: 10.1177/2055668319863557.
A. Chillura et al., “Advances in the rehabilitation of intensive care unit acquired weakness: A case report on the promising use of robotics and virtual reality coupled to physiotherapy,” Medicine, vol. 99, no. 28, 2020, doi: 10.1097/MD.0000000000020939.
F. Yakub, A. Z. Md. Khudzari, and Y. Mori, “Recent trends for practical rehabilitation robotics, current challenges and the future,” International Journal of Rehabilitation Research, vol. 37, no. 1, pp. 9–21, 2014, doi: 10.1097/MRR.0000000000000035.
T. Takebayashi, K. Takahashi, Y. Okita, H. Kubo, K. Hachisuka, and K. Domen, “Impact of the robotic-assistance level on upper extremity function in stroke patients receiving adjunct robotic rehabilitation: subanalysis of a randomized clinical trial,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, 2022, doi: 10.1186/s12984-022-00986-9.
F. Yuan, E. Klavon, Z. Liu, R. P. Lopez, and X. Zhao, “A systematic review of robotic rehabilitation for cognitive training,” Frontiers in Robotics and AI, vol. 8, 2021, doi: 10.3389/frobt.2021.605715.
K. Nizamis, A. Athanasiou, S. Almpani, C. Dimitrousis, and A. Astaras, “Converging robotic technologies in targeted neural rehabilitation: A review of emerging solutions and challenges,” Sensors, vol. 21, no. 6, 2021, doi: 10.3390/s21062084.
C. Giang, E. Pirondini, N. Kinany, C. Pierella, A. Panarese, M. Coscia, J. Miehlbradt, C. Magnin, P. Nicolo, A. Guggisberg, and S. Micera, “Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study,” BioMedical Engineering OnLine, vol. 19, no. 1, 2020, doi: 10.1186/s12938-020-00779-y.
P. V. V. Shahid Hussain, Prashant K. Jamwal and N. A. T. Brown, “Robot assisted ankle neuro-rehabilitation: State of the art and future challenges,” Expert Review of Neurotherapeutics, vol. 21, no. 1, pp. 111–121, 2020, doi: 10.3390/app112110174.
J. Bessler, G. B. Prange-Lasonder, L. Schaake, J. F. Saenz, C. Bidard, I. Fassi, M. Valori, A. B. Lassen, and J. H. Buurke, “Safety assessment of rehabilitation robots: A review identifying safety skills and current knowledge gaps,” Frontiers in Robotics and AI, vol. 8, 2021, doi: 10.3389/frobt.2021.602878.
A. Gupta, A. Singh, V. Verma, A. K. Mondal, and M. K. Gupta, “Developments and clinical evaluations of robotic exoskeleton technology for human upper-limb rehabilitation,” Advanced Robotics, vol. 34, no. 15, pp. 1023–1040, 2020, doi: 10.1080/01691864.2020.1749926.
D. Laparidou, F. Curtis, J. Akanuwe, K. Goher, A. Niroshan Siriwardena, and A. Kucukyilmaz, “Patient, carer, and staff perceptions of robotics in motor rehabilitation: a systematic review and qualitative meta-synthesis,” Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 1, 2021, doi: 10.1186/s12984-021-00976-3.
G. A. Albanese et al., “Robotic systems for upper-limb rehabilitation in multiple sclerosis: a swot analysis and the synergies with virtual and augmented environments,” Frontiers in Robotics and AI, vol. 11, 2024, doi: 10.3389/frobt.2024.1335147.
F. Romero-Sánchez, L. Luporini Menegaldo, J. M. Font-Llagunes, and M. Sartori, “Rehabilitation robotics: Challenges in design, control, and real applications,” Frontiers in neurorobotics, vol. 16, 2022, doi: 10.3389/978-2-88976-881-3.
C. G. Rose, A. D. Deshpande, J. Carducci, and J. D. Brown, “The road forward for upper-extremity rehabilitation robotics,” Current Opinion in Biomedical Engineering, vol. 19, 2021, doi: 10.1016/J.COBME.2021.100291.
A. Akbari, F. Haghverd, and S. Behbahani, “Robotic home-based rehabilitation systems design: From a literature review to a conceptual framework for community-based remote therapy during covid-19 pandemic,” Frontiers in Robotics and AI, vol. 8, 2021, doi: 10.3389/frobt.2021.612331.
F. van Dellen and R. Labruyère, “Settings matter: a scoping review on parameters in robot-assisted gait therapy identifies the importance of Ismail Ashiru Auta, Robot-Assisted Upper Limb Rehabilitation Using Imitation Learning Journal of Robotics and Control (JRC) ISSN: 2715-5072 99 reporting standards,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, 2022, doi: 10.1186/s12984-022-01017-3.
A. Garcia-Gonzalez, R. Q. Fuentes-Aguilar, I. Salgado, and I. Chairez, “A review on the application of autonomous and intelligent robotic devices in medical rehabilitation,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 44, no. 9, 2022, doi: 10.1007/s40430-022- 03692-8.
A. D. Banyai and C. Bris, an, “Robotics in physical rehabilitation: Systematic review,” Healthcare, vol. 12, no. 17, 2024, doi: 10.3390/healthcare12171720.
Z. Z. Major et al., “The impact of robotic rehabilitation on the motor system in neurological diseases. a multimodal neurophysiological approach,” International Journal of Environmental Research and Public Health, vol. 17, no. 18, 2020, doi: 10.3390/ijerph17186557.
M. Maaref, A. Rezazadeh, K. Shamaei, and M. Tavakoli, “A gaussian mixture framework for co-operative rehabilitation therapy in assistive impedance-based tasks,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 5, pp. 904–913, 2016, doi: 10.1109/JSTSP.2016.2532847.
Y. Meng, C. Munroe, Y.-N. Wu, and M. Begum, “A learning from demonstration framework to promote home-based neuromotor rehabilitation,” pp. 1126–1131, 2016, doi: 10.1109/ROMAN.2016.7745249.
C. Martinez and M. Tavakoli, “Learning and reproduction of therapist’s semi-periodic motions during robotic rehabilitation,” Robotica, vol. 38, no. 2, pp. 337–349, 2019, doi: 10.1017/S0263574719000651.
M. Sharifi, A. Zakerimanesh, J. K. Mehr, A. Torabi, V. K. Mushahwar, and M. Tavakoli, “Impedance variation and learning strategies in human–robot interaction,” IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6462– 6475, 2022, doi: 10.1109/TCYB.2020.3043798.
R. J. Escarabajal, J. L. Pulloquinga, P. Zamora-Ortiz, Á. Valera, V. Mata, and M. Vallés, “Imitation learning-based system for the execution of selfpaced robotic-assisted passive rehabilitation exercises,” IEEE Robotics and Automation Letters, vol. 8, no. 7, pp. 4283–4290, 2023, doi: 10.1109/LRA.2023.3281884.
M. Maaref, A. Rezazadeh, K. Shamaei, R. Ocampo, and T. Mahdi, “A bicycle cranking model for assist-as-needed robotic rehabilitation therapy using learning from demonstration,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 653–660, 2016, doi: 10.1109/LRA.2016.2525827.
S. Pareek and T. Kesavadas, “iART: Learning From Demonstration for Assisted Robotic Therapy Using LSTM," in IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 477-484, 2020, doi: 10.1109/LRA.2019.2961845.
S. F. Atashzar, M. Shahbazi, M. Tavakoli, and R. V. Patel, “A computational-model-based study of supervised haptics-enabled therapistin-the-loop training for upper-limb poststroke robotic rehabilitation,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 563–574, 2018, doi: 10.1109/TMECH.2018.2806918.
M. Saveriano, F. J. Abu-Dakka, A. Kramberger, and L. Peternel, “Dynamic movement primitives in robotics: A tutorial survey,” The International Journal of Robotics Research, vol. 42, no. 13, pp. 1133–1184, 2023, doi: 10.1177/02783649231201196.
L.-H. Kong, W. He, W.-S. Chen, H. Zhang, and Y.-N. Wang, “Dynamic movement primitives based robot skills learning,” Machine Intelligence Research, vol. 20, no. 3, pp. 396–407, 2023, doi: 10.1007/s11633-022- 1346-z.
Z. Lu, N. Wang and C. Yang, “A Dynamic Movement Primitives-Based Tool Use Skill Learning and Transfer Framework for Robot Manipulation," in IEEE Transactions on Automation Science and Engineering, 2024, doi: 10.1109/TASE.2024.3370139.
I. Seleem, H. El-Hussieny and S. Assal, “Development of a Demonstration-Guided Motion Planning for Multi-section Continuum Robots," 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 333-338, 2018, doi: 10.1109/SMC.2018.00066.
I. A. Seleem, H. El-Hussieny, and H. Ishii, “Imitation-based path planning and nonlinear model predictive control of a multi-section continuum robots,” Journal of Intelligent & Robotic Systems, vol. 108, no. 1, 2023, doi: 10.1007/s10846-023-01811-8.
I. A. Seleem, S. F. Assal, H. Ishii, and H. El-Hussieny, “Guided pose planning and tracking for multi-section continuum robots considering robot dynamics,” IEEE Access, vol. 7, pp. 166690–166703, 2019, doi: 10.1109/ACCESS.2019.2953122.
P. Pastor, H. Hoffmann, T. Asfour and S. Schaal, “Learning and generalization of motor skills by learning from demonstration," 2009 IEEE International Conference on Robotics and Automation, pp. 763-768, 2009, doi: 10.1109/ROBOT.2009.5152385.
C. Tamantini, M. Lapresa, F. Cordella, F. Scotto di Luzio, C. Lauretti, and L. Zollo, “Combined use of DMP and real objects in robot-aided rehabilitation,” 2020 I-RIM Conference, 2020, doi: 10.5281/zenodo.4781572.
C. Lauretti, F. Cordella, E. Guglielmelli, and L. Zollo, “Learning by demonstration for planning activities of daily living in rehabilitation and assistive robotics,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1375–1382, 2017, doi: 10.1109/LRA.2017.2669369.
A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: Learning attractor models for motor behaviors,” Neural Computation, vol. 25, no. 2, pp. 328–373, 2013, doi: 10.1162/NECO_a_00393.
W. Kim, C. Lee, and H. J. Kim, “Learning and generalization of dynamic movement primitives by hierarchical deep reinforcement learning from demonstration,” vol. 11, pp. 3117–3123, 2018, doi: 10.1109/IROS.2018.8594476.
H. Ravichandar, A. S. Polydoros, S. Chernova, and A. Billard, “Recent advances in robot learning from demonstration,” Annual review of control, robotics, and autonomous systems, vol. 3, no. 1, pp. 297–330, 2020, doi: 10.1146/annurev-control-100819-063206.
W. Si, N. Wang, and C. Yang, “A review on manipulation skill acquisition through teleoperation-based learning from demonstration,” Cognitive Computation and Systems, vol. 3, no. 1, pp. 1–16, 2021, doi: 10.1049/ccs2.12005.
S. Hu, R. Mendonca, M. J. Johnson, and K. J. Kuchenbecker, “Robotics for occupational therapy: Learning upper-limb exercises from demonstrations,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7781– 7788, 2021, doi: 10.1109/LRA.2021.3098945.
D. A. Duque, F. A. Prieto, and J. G. Hoyos, “Trajectory generation for robotic assembly operations using learning by demonstration,” Robotics and Computer-Integrated Manufacturing, vol. 57, pp. 292–302, 2019, doi: 10.1016/j.rcim.2018.12.007.
A. D. Sosa-Ceron, H. G. Gonzalez-Hernandez, and J. A. Reyes-Avendaño, “Learning from demonstrations in human–robot collaborative scenarios: A survey,” Robotics, vol. 11, no. 6, 2022, doi: 10.3390/robotics11060126.
Z. Lu, N. Wang, and C. Yang, “A constrained dmps framework for robot skills learning and generalization from human demonstrations,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 6, pp. 3265– 3275, 2021, doi: 10.1109/TMECH.2021.3057022.
J. Li, Z. Li, X. Li, Y. Feng, Y. Hu, and B. Xu, “Skill learning strategy based on dynamic motion primitives for human–robot cooperative manipulation,” IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 1, pp. 105–117, 2021, doi: 10.1109/TCDS.2020.3021762.
J. Li, M. Cong, D. Liu, and Y. Du, “Enhanced task parameterized dynamic movement primitives by gmm to solve manipulation tasks,” Robotic Intelligence and Automation, vol. 43, no. 2, pp. 85–95, 2023, doi: 10.1108/ria-07-2022-0199.
Y. Wang, W. Li, and Y. Liang, “A trajectory optimisation-based incremental learning strategy for learning from demonstration,” Applied Sciences, vol. 14, no. 11, 2024, doi: 10.3390/app14114943.
Z. Lu, N. Wang, and D. Shi, “Dmps-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation,” Complex & Intelligent Systems, vol. 8, no. 4, pp. 2873–2882, 2021, doi: 10.1007/s40747-021-00429-3.
A. Li, Z. Liu, W. Wang, M. Zhu, Y. Li, Q. Huo, and M. Dai, “Reinforcement learning with dynamic movement primitives for obstacle avoidance,” Applied Sciences, vol. 11, no. 23, 2021, doi: 10.3390/app112311184.
G. Li, Z. Jin, M. Volpp, F. Otto, R. Lioutikov, and G. Neumann, “Prodmp: A unified perspective on dynamic and probabilistic movement primitives,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 2325–2332, 2023, doi: 10.1109/LRA.2023.3248443.
S. Shaw, D. K. Jha, A. Raghunathan, R. Corcodel, D. Romeres, G. Konidaris, and D. Nikovski, “Constrained dynamic movement primitives for safe learning of motor skills,” arXiv 2022, doi: 10.48550/arXiv.2209.14461.
M. Ginesi, N. Sansonetto, and P. Fiorini, “Overcoming some drawbacks of dynamic movement primitives,” Robotics and Autonomous Systems, vol. 144, 2021, doi: 10.1016/j.robot.2021.103844.
S. Qiu, W. Guo, D. Caldwell, and F. Chen, “Exoskeleton online learning and estimation of human walking intention based on dynamical movement primitives,” IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 1, pp. 67–79, 2021, doi: 10.1109/TCDS.2020.2968845.
A. S. Anand, A. Østvik, E. I. Grøtli, M. Vagia and J. T. Gravdahl, “Realtime temporal adaptation of dynamic movement primitives for moving targets," 2021 20th International Conference on Advanced Robotics (ICAR), pp. 261-268, 2021, doi: 10.1109/ICAR53236.2021.9659384.
F. A. Zaini, M. F. Sulaima, I. A. W. A. Razak, N. I. Zulkafli, and H. Mokhlis, “A review on the applications of pso-based algorithm in demand side management: challenges and opportunities,” IEEE Access, vol. 11, pp. 53373–53400, 2023, doi: 10.1109/access.2023.3278261.
J. Cai, H. Wei, H. Yang, and X. Zhao, “A novel clustering algorithm based on dpc and pso,” IEEE Access, vol. 8, p. 88200–88214, 2020, doi: 10.1109/access.2020.2992903.
M. Jain, V. Saihjpal, N. Singh, and S. B. Singh, “An overview of variants and advancements of pso algorithm,” Applied Sciences, vol. 12, no. 17, 2022, doi: 10.3390/app12178392.
H. Moayedi, M. Mehrabi, M. Mosallanezhad, A. S. A. Rashid, and B. Pradhan, “Modification of landslide susceptibility mapping using optimized pso-ann technique,” Engineering with Computers, vol. 35, no. 3, p. 967–984, 2018, doi: 10.1007/s00366-018-0644-0.
A. Tharwat and W. Schenck, “A conceptual and practical comparison of pso-style optimization algorithms,” Expert Systems with Applications, vol. 167, 2021, doi: 10.1016/j.eswa.2020.114430.
L. Sciavicco and B. Siciliano, “Modelling and control of robot manipulators,” Measurement Science and Technology, vol. 11, no. 12, 2000, doi: 10.1088/0957-0233/11/12/709.
D. E. Whitney, “Resolved motion rate control of manipulators and human prostheses,” IEEE Transactions on Man-Machine Systems, vol. 10, no. 2, pp. 47–53, 1969, doi: 10.1109/TMMS.1969.299896.
B. Zhou, L. Yang, C. Wang, Y. Chen, and K. Chen, “Inverse jacobian adaptive tracking control of robot manipulators with kinematic, dynamic, and actuator uncertainties,” Complexity, vol. 2020, no. 1, 2020, doi: 10.1155/2020/5070354.
P. Chiacchio and B. Siciliano, “A closed-loop jacobian transpose scheme for solving the inverse kinematics of nonredundant and redundant wrists,” Journal of Robotic Systems, vol. 6, no. 5, pp. 601–630, 1989, doi: 10.1002/rob.4620060507.
C. M. Martinez, J. Fong, S. F. Atashzar, and M. Tavakoli, “Semiautonomous robot-assisted cooperative therapy exercises for a therapist’s interaction with a patient,” in 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019, pp. 1–5, doi: 10.1109/GlobalSIP45357.2019.8969143.
M. Najafi, K. Adams, and M. Tavakoli, “Robotic learning from demonstration of therapist’s time-varying assistance to a patient in trajectory-following tasks,” in 2017 International Conference on Rehabilitation Robotics (ICORR), 2017, pp. 888–894, doi: 10.1109/ICORR.2017.8009361.
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