A research paper by scientists at the City University of Hong Kong proposed a hybrid model-based and online data-driven control method for a tendon-driven continuum robot, which requires no prior dataset collection or training. The method incorporates the Jacobian derived from the piecewise constant curvature model with online Jacobian error compensation using a Kalman filter.

The new research paper, published on Aug. 7 in the journal Cyborg and Bionic Systems, presented an approach eliminates the need for offline dataset collection and training. Experiments conducted on a planar continuum manipulator demonstrate the effectiveness of the proposed method in improving tracking accuracy for both position and attitude. Additionally, the effects of various model parameters are analyzed through comparative experiments.

A research paper by scientists at Shanghai Jiao Tong University School of Medicine presented BioCompNet—an end-to-end deep learning workflow that integrates dual-parametric magnetic resonance imaging (MRI) sequences (water/fat) with a hierarchical U-Net architecture to enable fully automated quantification of 15 biomechanically critical BC components..

The research paper, published on Aug. 20, 2025 in the journal Cyborg and Bionic Systems.

Space manipulators play an important role in the on-orbit services and planetary surface operation whose reliability is a key issue. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. Since it is difficult for the manipulator to be repaired immediately, once it fails, it will mean that it cannot complete the mission as expected, which may cause serious losses and dangers. How to have a resilient guarantee, i.e., the manipulator’s ability to resiliently recover and continue to complete tasks, in failure or disturbance is the core capability of its future development. The motion planning unit is used as the computing terminal of the manipulator’s joint motion trajectory. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure.