Supernumerary Robotic Limbs (SRLs) can assist human motions by providing extra degrees of freedom (DoFs) and body support. The extra DoFs lead to larger design space in structure and control policies, which is complex and time-consuming with the traditional manual design process. In this pilot study, we proposed a novel morphology-controller co-optimization framework to automatically generate and optimize the SRL structure based on the locomotion task input. There are two layers, with the inner layer optimizing the controller to achieve human-robot synchronization, and the outer layer optimizing the morphology parameters for performance enhancement. We validated the proposed framework through simulations using SRLs in a load-bearing locomotion task. The results demonstrate that the controller optimization can automatically generate realistic gait patterns and stable human-robot synchronization, while the SRLs significantly improve the user's load-bearing capability. Additionally, the co-optimization process reduces both the manufacturing cost of the SRL and the torque on the joints. This approach shows potential for exhaustive exploration of the design space and acceleration of the design process. Future works will be done in a more realistic SRL generative design model and achieve Sim2Real for practical uses.

Representing human arm dynamic intent is essential for efective human-robot interaction. Accurately and robustly decoding these intentions through mathematical modeling of neuromuscular processes poses signifcant challenges. This study introduces an EIT-driven musculoskeletal model which integrates an EIT sensing system with methods for muscle identifcation, parameter estimation, and musculoskeletal system modeling. Unlike existing muscle-signal techniques, EIT captures muscle activities from the anatomical cross-sectional plane, providing both activation dynamics and morphological features. We validated our method through multi-DoF wrist kinematics estimation under varying contraction intensities, arm endpoint stifness estimation, and robotic variable admittance control. Our approach achieves accuracy comparable to state-of-the-art methods while requiring fewer training samples and a more compact sensing system. The model incorporates physiological constraints, minimizing decoding errors and ensuring interaction safety. This method enables reliable intent decoding with practical training demands. Future work will enhance the EIT system for complex tasks.

In this study, we proposed a novel human-machine interface (HMI) for robotic manipulator control. The specific target was to adjust the impedance coefficients of the robot controller in real time by measuring the human forearm muscle contractions. We firstly designed a HMI system. Different from the frequently used sEMG technologies, the interface in our study could detect muscle morphological changes within the skin by the electrical impedance tomography (EIT). The sensing front-end was a soft elastic fabric band which was compatible to different arm shapes. With the specific designed sensing hardware and the re-construction algorithms, EIT images indicating forearm muscle shapes were obtained. We then designed a hybrid positon/impedance controller on a UR5 with the impedance coefficients being tuned in real time by the grasp force estimation. A sigmoid regression algorithm was used to map the EIT images to the grasp forces. After implementation of the whole system, two experiments were carried out. The first experiment was the off-line grasp force estimation. With the 1:1 cross validation, an average R2of 0.83±0.04 and an average of the relative root mean square error (RRMSE) of 0.31±0.10 across 5 subjects were yielded. The second experiment was the real-time robot control. Trajectory tracking task with dynamic uncertainties were investigated and grasp forces were estimated in real-time. With higher muscle contraction levels, smaller position errors were observed and shorter time was needed to return to the expected trajectory when there were external disturbances. The results proved the feasibility of the new approach on human-robot interaction tasks. Future endeavours will be made to get more promising results.