This paper proposes a data-driven method for powered prosthesis control that achieves stable walking without the need for additional sensors on the human. The key idea is to extract the nominal gait and the human interaction information from motion capture data, and reconstruct the walking behavior with a dynamic model of the human-prosthesis system. The walking behavior of a human wearing a powered prosthesis is obtained through motion capture, which yields the limb and joint trajectories. Then a nominal trajectory is obtained by solving a gait optimization problem designed to reconstruct the walking behavior observed by motion capture. Moreover, the interaction force profiles between the human and the prosthesis are recovered by simulating the model following the recorded gaits, which are then used to construct a force tube that covers all the interaction force profiles. Finally, a robust Control Lyapunov Function (CLF) Quadratic Programming (QP) controller is designed to guarantee the convergence to the nominal trajectory under all possible interaction forces within the tube. Simulation results show this controller's improved tracking performance with a perturbed force profile compared to other control methods with less model information.
Control of Powered Ankle-Foot Prostheses on Compliant Terrain: A Quantitative Approach to Stability Enhancement
Mechanical Engineering Department, at the University of Delaware（德克萨斯大学达勒姆分校机械工程系）；Mechanical Engineering Department, at the Rice University（赖斯大学机械工程系）
People with lower-limb loss, the majority of which use passive prostheses, exhibit a high incidence of falls each year. Powered lower-limb prostheses have the potential to reduce fall rates by actively helping the user recover from a stumble, but the unpredictability of the human response makes it difficult to design controllers that ensure a successful recovery. This paper presents a method called TRIP-RTD (Trip Recovery in Prostheses via Reachability-based Trajectory Design) for online trajectory planning in a knee prosthesis during and after a stumble that can accommodate a set of possible …
Nonlinear control methodologies have successfully realized stable human-like walking on powered prostheses. However, these methods are typically restricted to model independent controllers due to the unknown human dynamics acting on the prosthesis. This paper overcomes this restriction by introducing the notion of a separable subsystem control law, independent of the full system dynamics. By constructing an equivalent subsystem, we calculate the control law with local information. We build a subsystem model of a general open-chain manipulator to demonstrate the control method's applicability. …
Lower-limb prosthesis wearers are more prone to falling than non-amputees. Powered prostheses can reduce this instability of passive prostheses. While shown to be more stable in practice, powered prostheses generally use model-independent control methods that lack formal guarantees of stability and rely on heuristic tuning. Recent work overcame one of the limitations of model-based prosthesis control by developing a class of provably stable prosthesis controllers that only require the human interaction forces with the prosthesis, yet these controllers have not been realized with sensing of the…
Synergistic prostheses enable the coordinated movement of the human-prosthetic arm, as required by activities of daily living. This is achieved by coupling the motion of the prosthesis to the human command, such as the residual limb movement in motion-based interfaces. Previous studies demonstrated that developing human-prosthetic synergies in joint-space must consider individual motor behaviour and the intended task to be performed, requiring personalisation and task calibration. In this work, an alternative synergy-based strategy, utilising a synergistic relationship expressed in task-space,…
Ankle push-off largely contributes to limb energy generation in human walking, leading to smoother and more efficient locomotion. Providing this net positive work to an amputee requires an active prosthesis, but has the potential to enable more natural assisted locomotion. To this end, this paper uses multi-contact models of locomotion together with force-based nonlinear optimization-based controllers to achieve human-like kinematic behavior, including ankle push-off, on a powered transfemoral prosthesis for 2 subjects. In particular, we leverage model-based control approaches for dynamic bipe…
National University of Science and Technology (NUST)（巴基斯坦国立科技大学）
The issues of research required in the field of bio medical engineering and externally-powered prostheses are attracting attention of regulatory bodies and the common people in various parts of the globe. Today, 90 percent of prostheses used are conventional body powered cable-controlled ones which are very uncomfortable to the amputees as fairly large amount of forces and excursions have to be generated by the amputee. Additionally, its amount of rotation is limited. Alternatively, prosthetic limbs driven using electrical motors might deliver added functionality and improved control, accompan…
Tactile Perception in Upper Limb Prostheses: Mechanical Characterization, Human Experiments, and Computational Findings
Istituto Italiano di Tecnologia（意大利技术研究院）；Centro di ricerca E. Piaggio, University of Pisa（比萨大学E.皮亚焦研究中心）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）；Department of Mechanical Engineering and Bioengineering Program, University of California, Santa Barbara（加州大学圣巴巴拉分校机械工程与生物工程项目）
Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta（阿尔伯塔大学物理医学与康复科，医学部）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；Faculty of Rehabilitation Medicine（康复医学学院）；Department of Biomedical Engineering, University of Alberta（阿尔伯塔大学生物医学工程系）；Faculty of Kinesiology, Sport, and Recreation（运动、体育与休闲学院）
Southern University of Science and Technology（南方科技大学）；National University of Singapore（新加坡国立大学）
Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the…
University of Texas at Dallas（德克萨斯大学达拉斯分校）；San Francisco State University（旧金山州立大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Although there has been recent progress in control of multi-joint prosthetic legs for rhythmic tasks such as walking, control of these systems for non-rhythmic motions and general real-world maneuvers is still an open problem. In this article, we develop a new controller that is capable of both rhythmic (constant-speed) walking, transitions between speeds and/or tasks, and some common volitional leg motions. We introduce a new piecewise holonomic phase variable, which, through a finite state machine, forms the basis of our controller. The phase variable is constructed by measuring the thigh an…
Prosthetic Hand Manipulation System Based on EMG and Eye Tracking Powered by the Neuromorphic Processor AltAi
Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）
University of Minnesota（明尼苏达大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Nerves Incorporated（Nerves公司）；Fasikl Incorporated（Fasikl公司）
Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor …
Multiarticulate bionic arms are now capable of mimicking the endogenous movements of the human hand. 3D-printing has reduced the cost of prosthetic hands themselves, but there is currently no low-cost alternative to dexterous electromyographic (EMG) control systems. To address this need, we developed an inexpensive (~$675) and portable EMG control system by integrating low-cost microcontrollers with an EMG acquisition device. We validated signal acquisition by comparing the signal-to-noise ratio (SNR) of our system with that of a high-end research-grade system. We also demonstrate the ability …
Artificial and Mechanical Intelligence, Istituto Italiano di Tecnologia（人工智能与机械智能，意大利技术研究院）；School of Computer Science, University of Manchester（计算机科学学院，曼彻斯特大学）；Department of Industrial Engineering, University of Naples Federico II（工业工程系，那不勒斯费德里克二世大学）
Impedance controllers are popularly used in the field of lower limb prostheses and exoskeleton development. Such controllers assume the joint to be a spring-damper system described by a discrete set of equilibria and impedance parameters. Said parameters are estimated via a least squares optimization that minimizes the difference between the controller's output torque and human joint torque. Other researchers have used perturbation studies to determine empirical values for ankle impedance. The resulting values vary greatly from the prior least squares estimates. While perturbation studies are …
End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance
Georgia Institute of Technology（佐治亚理工学院）；Woodruff School of Mechanical Engineering（伍德拉夫机械工程学院）；Institute for Robotics and Intelligent Machines（机器人与智能机器研究所）
Would you let a humanoid play storytelling with your child? A usability study on LLM-powered narrative Human-Robot Interaction
Humanoid Sensing and Perception, Italian Institute of Technology (IIT)（人形感知与感知，意大利技术研究院）；Social Cognition in Human-Robot Interaction, IIT（人机交互中的社会认知，IIT）
CAD-Driven Co-Design for Flight-Ready Jet-Powered Humanoids
Artificial and Mechanical Intelligence, Istituto Italiano di Tecnologia, Genoa, Italy（人工智能与机械智能研究所，热那亚，意大利）；School of Computer Science, University of Manchester, Manchester, UK（计算机科学学院，曼彻斯特大学，英国）
University of Michigan（密歇根大学）；University of Texas at Dallas（得克萨斯大学达拉斯分校）；Shirley Ryan AbilityLab（雪莉瑞安能力实验室）；University of Texas at Austin（得克萨斯大学奥斯汀分校）；Northwestern University（西北大学）
Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a…
Institute of Neuroinformatics, University of Zurich（苏黎世大学神经信息学研究所）；ETH Zurich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience. This paper describes the first steps toward learning complex controllers for dynamical robotic assistive devices. We provide the first example of behavioral cloning to control a powered transfemoral prostheses using a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) running on a custom hardware accelerator that exploits temporal sparsity. The RNN is trained on da…
Southern University of Science and Technology（南方科技大学）；The University of British Columbia（英属哥伦比亚大学）
This survey paper concerns Sensor Fusion for Predictive Control of Human-Prosthesis-Environment Dynamics in Assistive Walking. The powered lower limb prosthesis can imitate the human limb motion and help amputees to recover the walking ability, but it is still a challenge for amputees to walk in complex environments with the powered prosthesis. Previous researchers mainly focused on the interaction between a human and the prosthesis without considering the environmental information, which can provide an environmental context for human-prosthesis interaction. Therefore, in this review, recent s…
Evolutionary Continuous Adaptive RL-Powered Co-Design for Humanoid Chin-Up Performance
Robotics Innovation Center, DFKI GmbH（DFKI机器人创新中心）；Computational Robotics Lab, ETH Zurich（苏黎世联邦理工学院计算机器人实验室）；AG Robotik University of Bremen（不莱梅大学机器人技术研究所）；RWTH Aachen University（亚琛工业大学）
Human-like motion is a primary goal for many robotic assistive devices. Emulating the strategy of the human neuromuscular system may aid the control of such powered devices, yet many challenges remain. In this study, we investigated the potential for using the winding filament model (WFM) of muscle to predict the net muscle moment of the ankle. The long-term goal is to use this model to improve ankle control of a commercial powered exoskeleton. The innovation aspects of this study are: First, there have been no commercialized active ankle exoskeletons available in the market. All the available…
Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity datab…
Unified Multi-Rate Model Predictive Control for a Jet-Powered Humanoid Robot
Artificial and Mechanical Intelligence, Istituto Italiano di Tecnologia（人工智能与机械智能研究所）；School of Computer Science, University of Manchester（曼彻斯特大学计算机科学学院）
University of Birmingham（伯明翰大学）；Technology Innovation Institute（技术创新研究院）；Imperial College London（伦敦帝国理工学院）
Adapting upper-limb impedance (i.e., stiffness, damping, inertia) is essential for humans interacting with dynamic environments for executing grasping or manipulation tasks. On the other hand, control methods designed for state-of-the-art upper-limb prostheses infer motor intent from surface electromyography (sEMG) signals in terms of joint kinematics, but they fail to infer and use the underlying impedance properties of the limb. We present a framework that allows a human user to simultaneously control the kinematics, stiffness, and damping of a simulated robot through wrist's flexion-extensi…
AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment
Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control
Human-Robot Interfaces and Interaction Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy（人机交互实验室，意大利技术研究院，热那亚，意大利）；Dept. of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy（信息学、生物工程、机器人与系统工程系，热那亚大学，热那亚，意大利）；Airworks SRL, Povoletto, Italy（Airworks SRL，意大利）
A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions
German Aerospace Center (DLR)（德国航空航天中心）；Korea Institute of Robot and Convergence (KIRO)（韩国机器人与融合研究院）；Samsung Electronics（三星电子）；POSCO Technical Research Laboratories（浦项制铁技术研究实验室）
Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs
Dalle Molle Institute for Artificial Intelligence (IDSIA), University of Lugano & SUPSI（达勒·莫勒人工智能研究所（IDSIA），卢加诺大学与瑞士南部应用科学大学）；University of Bologna（博洛尼亚大学）；ETH Zürich（苏黎世联邦理工学院）
