The integration of robots into environments shared by humans has been enhanced through the use of redundant robots capable of executing primary tasks and secondary objectives such as obstacle avoidance and null-space impedance control. A critical secondary objective involves optimizing manipulator configurations to reduce torque and prevent torque saturation, similar to how athletes distribute loads to minimize the risk of injury. This paper suggests employing robotic redundancy to evenly distribute joint loads, thereby improving performance and avoiding torque saturation. Prior studies primarily focused on either endpoint stiffness control or kinetic energy minimization, each having its drawbacks. This paper introduces a novel objective function that responds to all external disturbances at the end-effector, aiming to lower joint torques via redundancy for precise trajectory tracking amidst disturbances. This method, which provides an inverse kinematics solution adaptable to various controllers, demonstrated a 29.85% reduction in peak torque and a 14.69% decrease in cumulative torques in the KUKA LBR iiwa 14 R820 robot.

A majority of robotic gait trainers to facilitate physical therapy for gait rehabilitation in humans are based on multidegree-of-freedom exoskeleton-based systems with sophisticated electro-mechanical hardware and software, and consequently remain inaccessible to vast sections of the populations around the world. This study seeks to advance the development of a single degree-of-freedom (DOF) gait trainer for gait therapy for individuals with neuromuscular impairments. The goal is to offer a cost-effective, accessible solution to cater to the global need for gait rehabilitation. We build upon the previous gait trainer design based on Jansen mechanism and provide an in-depth analysis and experimental validation of its kinematic performance. The device's performance is also tested and successfully demonstrated through trials involving two healthy individuals to examine its kinematic behavior under human-induced load conditions. The gait trainer demonstrates satisfactory performance under both no load conditions and a 2 kg load, exhibiting an area difference of 1% and 7%, respectively. However, when subjected to a 5 kg loading condition, a significant area difference of 27% is observed, primarily attributed to the cantilever loading at the driving shaft. A method to adjust link lengths based on specific human gait trajectories is proposed and validated. Additionally, a cost-effective tool for ankle trajectory measurement is introduced to establish a ground truth. The study demonstrates the potential of an affordable, single DOF gait trainer in facilitating high-volume therapy for those with walking disorders. This research represents a step toward making gait therapy more accessible worldwide.

Background and aim: Parkinson’s disease (PD), a neuro-degenerative disorder, is often detected by the onset of its motor symptoms such as rest tremor. Unfortunately, motor symptoms appear only when approximately 40–60% of the dopaminergic neurons in the substantia nigra are lost. In most cases, by the time PD is clinically diagnosed, the disease may already have started 4 to 6 years beforehand. There is therefore a need for developing a test for detecting PD before the onset of motor symptoms. This phase of PD is referred to as Presymptomatic PD (PPD). The motor symptoms of Parkinson’s Disease are manifestations of instability in the sensorimotor system that develops gradually due to the neurodegenerative process. In this paper, based on the above insight, we propose a new method that can potentially be used to detect the degradation of motor control stability, which can be employed for the detection of PPD. Methods: The proposed method tracks the tendency of a feedback control system to transition to an unstable state and uses a machine learning algorithm for its robust detection. This method is explored using a simple simulation example consisting of a simple pendulum with a proportional-integral-derivative (PID) controller as a conceptual representation for both healthy and PPD individuals with a noise variance of 0.01 and a noise variance of 0.1. The present study adopts a longitudinal design to evaluate the effectiveness of the proposed methodology. Specifically, the performance of the proposed approach, with specific choices of features, is compared to that of the Support Vector Machine (SVM) algorithm for machine learning under conditions of incremental delay-induced instability. This comparison is made with results obtained using the Longitudinal Support Vector Machine (LSVM) algorithm for machine learning, which is better suited for longitudinal studies. Results: The results of SVM with one choice of features are comparable with the results of LSVM for a noise variance of 0.01. These results are almost unaffected by a noise variance of 0.1. All of the methods showed a high sensitivity above 96% and specificity above 98% on a training data set. In addition, they perform very well with the validation synthetic data set with sensitivity above 95% and specificity above 98%. These results are robust to further increases in noise variance representing the large variances expected in patient populations. Conclusions: The proposed method is evaluated on a synthetic data set, and the machine learning results show a promise and potential for use for detecting PPD through an early diagnostic device. In addition, an example task with physiological measurement that can potentially be used as a clinical movement control test along with representative data from both healthy individuals and PD patients is also presented, demonstrating the feasibility of performing a longitudinal study to validate and test the robustness of the proposed method.

Understanding human collaborative behavior in tasks with physical interaction is essential for advancing physical human-robot collaboration. Investigating how individuals learn to collaborate over repeated interactions can provide valuable insights for developing robotic agents capable of gradually improving coordination and collaboration performance. Therefore, this study investigated learning behavior in a high-precision task over repeated haptic collaboration. Specifically, we examined if learned collaboration behavior is partner-specific, what collaboration strategies are developed, and if interpersonal differences affect collaboration. Our results indicate that repeated physical collaboration with the same partner allowed for immediate high performance with a familiar partner in subsequent collaborations, whereas adapting to an unfamiliar partner required retraining. Participants used partner-specific collaboration behaviors—in terms of motions and forces—that could be retained in subsequent interactions. Collaborators reduced the variability of their arm motions over repeated collaboration, achieving higher performance, likely due to increased predictability. Collaboration also enabled knowledge transfer between partners, with individual improvement being enhanced when paired with a better-performing partner. These findings suggest that partners in a collaborative precision task optimize their performance by gradually negotiating a joint action strategy, which is reused in subsequent collaborations with familiar partners and carries over to solo task execution.

In recent years, robots with higher levels of sophistication in controlling the mechanical impedance of interaction have received increasing attention. These robots can precisely move and manipulate objects in predetermined situations by exploiting impedance control. However, this requires that the desired impedance of interaction be factored into the control design process beforehand, and interaction impedance is typically not dynamically modulated traditionally. In contrast, humans are skilled at modulating mechanical impedance, mainly mechanical stiffness, in their interactions with their surroundings. This ability is vital for successful interaction with the environment. This human behaviour has inspired various variable impedance learning controllers for tool manipulation. However, humans not only demonstrate the ability to stabilise interaction with a divergent force-field but also demonstrate a few higher-level learning features. Savings is one such feature, and it refers to people being able to learn faster when the same divergent-field is applied again. Retention is another essential feature, allowing humans to initially demonstrate lower error when the same exposure to a divergent field (perturbation) is applied again. These features have practical utility in the robotic application and have not been explored, this paper presents a novel approach to variable impedance learning control for robots inspired by human motor learning mechanisms. We combine iterative learning control, feedforward control, and ideas from neuroscience to synthesise the controller. The proposed method aims to vary impedance to stabilise the interaction with divergent-field while incorporating savings and retention features. Simulation results with a two-link serial chain manipulator demonstrate the proposed method’s efficacy, savings, and retention.