@article{li_liu_si_stallrich_huang_2023, title={Hierarchical Optimization for Control of Robotic Knee Prostheses Toward Improved Symmetry of Propulsive Impulse}, volume={70}, ISSN={["1558-2531"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85142785855&partnerID=MN8TOARS}, DOI={10.1109/TBME.2022.3224026}, abstractNote={Automatically personalizing complex control of robotic prostheses to improve gait performance, such as gait symmetry, is challenging. Recently, human-in-the-loop (HIL) optimization and reinforcement learning (RL) have shown promise in achieving optimized control of wearable robots for each individual user. However, HIL optimization methods lack scalability for high-dimensional space, while RL has mostly focused on optimizing robot kinematic performance. Thus, we propose a novel hierarchical framework to personalize robotic knee prosthesis control and improve overall gait performance. Specifically, in this study the framework was implemented to simultaneously design target knee kinematics and tune 12 impedance control parameters for improved symmetry of propulsive impulse in walking. In our proposed framework, HIL optimization is used to identify an optimal target knee kinematics with respect to symmetry improvement, while RL is leveraged to yield an optimal policy for tuning impedance parameters in high-dimensional space to match the kinematics target. The proposed framework was validated on human subjects, walking with robotic knee prosthesis. The results showed that our design successfully shaped the target knee kinematics as well as configured 12 impedance control parameters to improve propulsive impulse symmetry of the human users. The knee kinematics that yielded best propulsion symmetry did not preserve the normative knee kinematics profile observed in non-disabled individuals, suggesting that restoration of normative joint biomechanics in walking does not necessarily optimize the gait performance of human-prosthesis systems. This new framework for prosthesis control personalization may be extended to other wearable devices or different gait performance optimization goals in the future.}, number={5}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Li, Minhan and Liu, Wentao and Si, Jennie and Stallrich, Jonathan W. and Huang, He}, year={2023}, month={May}, pages={1634–1642} }
 @article{li_zhong_lobaton_huang_2022, title={Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode}, volume={30}, ISSN={["1558-0210"]}, url={https://doi.org/10.1109/TNSRE.2022.3168796}, DOI={10.1109/TNSRE.2022.3168796}, abstractNote={Predicting the user’s intended locomotion mode is critical for wearable robot control to assist the user’s seamless transitions when walking on changing terrains. Although machine vision has recently proven to be a promising tool in identifying upcoming terrains in the travel path, existing approaches are limited to environment perception rather than human intent recognition that is essential for coordinated wearable robot operation. Hence, in this study, we aim to develop a novel system that fuses the human gaze (representing user intent) and machine vision (capturing environmental information) for accurate prediction of the user’s locomotion mode. The system possesses multimodal visual information and recognizes user’s locomotion intent in a complex scene, where multiple terrains are present. Additionally, based on the dynamic time warping algorithm, a fusion strategy was developed to align temporal predictions from individual modalities while producing flexible decisions on the timing of locomotion mode transition for wearable robot control. System performance was validated using experimental data collected from five participants, showing high accuracy (over 96% in average) of intent recognition and reliable decision-making on locomotion transition with adjustable lead time. The promising results demonstrate the potential of fusing human gaze and machine vision for locomotion intent recognition of lower limb wearable robots.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Li, Minhan and Zhong, Boxuan and Lobaton, Edgar and Huang, He}, year={2022}, pages={1103–1112} }
 @article{wu_li_yao_liu_si_huang_2022, title={Reinforcement Learning Impedance Control of a Robotic Prosthesis to Coordinate With Human Intact Knee Motion}, volume={7}, ISSN={["2377-3766"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85131742245&partnerID=MN8TOARS}, DOI={10.1109/LRA.2022.3179420}, abstractNote={This study aims to demonstrate reinforcement learning tracking control for automatically configuring the impedance parameters of a robotic knee prosthesis. While our previous studies involving human subjects have focused on tuning the impedance control parameters to meet a fixed, subjectively prescribed target motion profile to enable continuous walking with human-in-the-loop, in this paper we develop a new tracking control solution for a robotic knee to mimic the motion of the intact knee. As such, we replaced the prescribed target knee motion by an automatically generated profile based on the intact knee. As the profile of the intact knee varies over time due to human adaptation, we are presented with a challenging tracking control problem in the context of classical control theory. By formulating the “echo control” of the robotic knee as a reinforcement learning problem, we provide a promising new tool for real-time tracking control design without explicitly representing the underlying dynamics using a mathematical model, which can be difficult to obtain for a human-robot system. Additionally, our results may inspire future studies and new robotic prosthesis impedance control designs that can potentially coordinate between the intact and the robotic limbs toward daily use of the robotic device.}, number={3}, journal={IEEE ROBOTICS AND AUTOMATION LETTERS}, author={Wu, Ruofan and Li, Minhan and Yao, Zhikai and Liu, Wentao and Si, Jennie and Huang, He}, year={2022}, month={Jul}, pages={7014–7020} }
 @article{tu_li_liu_si_huang_2021, title={A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton}, volume={2021-May}, ISSN={["2577-087X"]}, url={http://dx.doi.org/10.1109/icra48506.2021.9562062}, DOI={10.1109/ICRA48506.2021.9562062}, abstractNote={Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel data-driven solution framework based on reinforcement learning (RL), without first modeling the human-robot dynamics, to provide optimal and adaptive personalized torque assistance for reducing human efforts during walking. Our automatic personalization solution framework includes the assistive torque profile with two control timing parameters (peak and offset timings), the least square policy iteration (LSPI) for learning the parameter tuning policy, and a cost function based on a transferred work ratio. The proposed controller was successfully validated on a healthy human subject to assist unilateral hip extension in walking. The results showed that the optimal and adaptive RL controller as a new approach was feasible for tuning assistive torque profile of the hip exoskeleton that coordinated with human actions and reduced activation level of hip extensor muscle in human.}, journal={2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)}, publisher={IEEE}, author={Tu, Xikai and Li, Minhan and Liu, Ming and Si, Jennie and Huang, He}, year={2021}, pages={10610–10616} }
 @article{zhong_silva_li_huang_lobaton_2021, title={Environmental Context Prediction for Lower Limb Prostheses With Uncertainty Quantification}, volume={18}, ISSN={["1558-3783"]}, url={https://doi.org/10.1109/TASE.2020.2993399}, DOI={10.1109/TASE.2020.2993399}, abstractNote={Reliable environmental context prediction is critical for wearable robots (e.g., prostheses and exoskeletons) to assist terrain-adaptive locomotion. This article proposed a novel vision-based context prediction framework for lower limb prostheses to simultaneously predict human’s environmental context for multiple forecast windows. By leveraging the Bayesian neural networks (BNNs), our framework can quantify the uncertainty caused by different factors (e.g., observation noise, and insufficient or biased training) and produce a calibrated predicted probability for online decision-making. We compared two wearable camera locations (a pair of glasses and a lower limb device), independently and conjointly. We utilized the calibrated predicted probability for online decision-making and fusion. We demonstrated how to interpret deep neural networks with uncertainty measures and how to improve the algorithms based on the uncertainty analysis. The inference time of our framework on a portable embedded system was less than 80 ms/frame. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. Note to Practitioners—This article was motivated by two practical problems in computer vision for wearable robots: First, the performance of deep neural networks is challenged by real-life disturbances. However, reliable confidence estimation is usually unavailable and the factors causing failures are hard to identify. Second, evaluating wearable robots by intuitive trial and error is expensive due to the need for human experiments. Our framework produces a calibrated predicted probability as well as three uncertainty measures. The calibrated probability makes it easy to customize prediction decision criteria by considering how much the corresponding application can tolerate error. This study demonstrated a practical procedure to interpret and improve the performance of deep neural networks with uncertainty quantification. We anticipate that our methodology could be extended to other applications as a general scientific and efficient procedure of evaluating and improving intelligent systems.}, number={2}, journal={IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhong, Boxuan and Silva, Rafael Luiz and Li, Minhan and Huang, He and Lobaton, Edgar}, year={2021}, month={Apr}, pages={458–470} }
 @article{gao_si_wen_li_huang_2021, title={Reinforcement Learning Control of Robotic Knee With Human-in-the-Loop by Flexible Policy Iteration}, volume={5}, ISSN={["2162-2388"]}, url={http://dx.doi.org/10.1109/tnnls.2021.3071727}, DOI={10.1109/TNNLS.2021.3071727}, abstractNote={We are motivated by the real challenges presented in a human–robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic programming (ADP) results that consider system performance theoretically are not readily providing practically useful learning control algorithms for this problem, and reinforcement learning (RL) algorithms that address the issue of data efficiency usually do not have performance guarantees for the controlled system. This study fills these important voids by introducing innovative features to the policy iteration algorithm. We introduce flexible policy iteration (FPI), which can flexibly and organically integrate experience replay and supplemental values from prior experience into the RL controller. We show system-level performances, including convergence of the approximate value function, (sub)optimality of the solution, and stability of the system. We demonstrate the effectiveness of the FPI via realistic simulations of the human–robot system. It is noted that the problem we face in this study may be difficult to address by design methods based on classical control theory as it is nearly impossible to obtain a customized mathematical model of a human–robot system either online or offline. The results we have obtained also indicate the great potential of RL control to solving realistic and challenging problems with high-dimensional control inputs.}, number={10}, journal={IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Gao, Xiang and Si, Jennie and Wen, Yue and Li, Minhan and Huang, He}, year={2021}, month={May} }
 @article{huang_si_brandt_li_2021, title={Taking both sides: seeking symbiosis between intelligent prostheses and human motor control during locomotion}, volume={20}, ISSN={["2468-4511"]}, url={http://dx.doi.org/10.1016/j.cobme.2021.100314}, DOI={10.1016/j.cobme.2021.100314}, abstractNote={Robotic lower-limb prostheses aim to replicate the power-generating capability of biological joints during locomotion to empower individuals with lower-limb loss. However, recent clinical trials have not demonstrated clear advantages of these devices over traditional passive devices. We believe this is partly because the current designs of robotic prothesis controllers and clinical methods for fitting and training individuals to use them do not ensure good coordination between the prosthesis and user. Accordingly, we advocate for new holistic approaches in which human motor control and intelligent prosthesis control function as one system (defined as human-prosthesis symbiosis). We hope engineers and clinicians will work closely to achieve this symbiosis, thereby improving the functionality and acceptance of robotic prostheses and users' quality of life.}, journal={CURRENT OPINION IN BIOMEDICAL ENGINEERING}, publisher={Elsevier BV}, author={Huang, He and Si, Jennie and Brandt, Andrea and Li, Minhan}, year={2021}, month={Dec} }
 @article{li_wen_gao_si_huang_2021, title={Toward Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control}, volume={5}, ISSN={["1941-0468"]}, url={http://dx.doi.org/10.1109/tro.2021.3078317}, DOI={10.1109/TRO.2021.3078317}, abstractNote={Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is to improve tuning efficiency and speed it up for the human user, in clinic settings, while maintaining human safety. We, therefore, propose a policy iteration with constraint embedded (PICE) method as an innovative solution to the problem under the framework of reinforcement learning. Central to PICE is the use of a projected Bellman equation with a constraint of assuring positive semidefiniteness of performance values during policy evaluation. Additionally, we developed both online and offline PICE implementations that provide additional flexibility for the designer to fully utilize measurement data, either from on-policy or off-policy, to further improve PICE tuning efficiency. Our human subject testing showed that the PICE provided effective policies with significantly reduced tuning time. For the first time, we also experimentally evaluated and demonstrated the robustness of the deployed policies by applying them to different tasks and users. Putting it together, our new way of problem solving has been effective as PICE has demonstrated its potential toward truly automating the process of control parameter tuning for robotic knee prosthesis users.}, number={1}, journal={IEEE TRANSACTIONS ON ROBOTICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Li, Minhan and Wen, Yue and Gao, Xiang and Si, Jennie and Huang, He}, year={2021}, month={May} }
 @article{alili_nalam_li_liu_si_huang_2021, title={User Controlled Interface for Tuning Robotic Knee Prosthesis}, ISSN={["2153-0858"]}, url={http://dx.doi.org/10.1109/iros51168.2021.9636264}, DOI={10.1109/IROS51168.2021.9636264}, abstractNote={The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed.}, journal={2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)}, publisher={IEEE}, author={Alili, Abbas and Nalam, Varun and Li, Minhan and Liu, Ming and Si, Jennie and Huang, He}, year={2021}, pages={6190–6195} }
 @article{wen_li_si_huang_2020, title={Wearer-Prosthesis Interaction for Symmetrical Gait: A Study Enabled by Reinforcement Learning Prosthesis Control}, volume={28}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85083163810&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2020.2979033}, abstractNote={With advances in robotic prostheses, rese-archers attempt to improve amputee’s gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis’ gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers’ gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop. Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry: the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer’s motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.}, number={4}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Wen, Yue and Li, Minhan and Si, Jennie and Huang, He}, year={2020}, month={Apr}, pages={904–913} }