The accurate position transmission of tendon-sheath mechanisms (TSMs) is challenging but of significance to the flexible robot for minimally invasive surgery (MIS). The challenges are mainly attributed to 1) the tendon-elongation and its caused hysteresis that depend on the route configuration of the TSM and could result in misaligned position transmission, 2) realistic surgical scenarios requiring the TSM with arbitrary and even time-varying route configurations, and 3) the absence of distal sensory feedback due to strict spatial constraints. Existing works are always devoted to tackling 1) yet evade 2) and 3). Here a route-related tendon-elongation model is formulated to resolve 1), and in response to 2), a route-sensing optical fiber is used. Obeying 3), a feedforward hysteresis compensator is then developed to align the distal position of the tendon with the desired position. Our final contribution gives an application-oriented remedy for the foregoing methodologies. Applying our compensator on the challenging position transmission tasks subject to 2) and 3), the positional accuracy can be still maintained at around 97.50%; guided by the provided remedy, the surgical end-effector achieves sub-millimeter tip position accuracy. Extensive tests demonstrate that the pending concerns yet of great practical importance in existing related works are well resolved.

Accurate control of continuum robots is challenging, especially in the presence of external disturbances. To address this issue, reinforcement learning (RL) has been increasingly investigated in continuum robot control due to its online policy updating capability. However, the gap in Sim2Real transfer caused by inaccurate modeling results in RL implementation a dilemma. In this article, we propose a safety-critical Real2Sim2Real online RL framework, where the Real2Sim transfer is first achieved by the identification of a continuum robot using the Koopman operator. We further improve the training efficiency by introducing an imperfect demonstration into the RL framework. The offline policy is trained in simulations and then tested on a real continuum robot platform. During tests, the tracking performance is influenced by the hysteresis effect that cannot be captured by the Koopman operator. This results in a millimeter-level tracking root mean square error (RMSE). To address this issue, we online update the policy as well as the model, and the RMSE of the online controller outperforms the offline controller by 89.16% in free space and 85.70% under external payload, respectively.

Owing to their high power‐to‐weight ratio and inherent flexibility, tendon–sheath mechanisms (TSMs) have been extensively adopted in robotic systems that demand compact designs and adaptability to complex environments. However, nonlinear friction between the tendon and the sheath inevitably induces tendon‐elongation and backlash‐like hysteresis, making accurate position transmission control in TSMs highly challenging. This short communication critically reviews representative strategies developed to address this issue, identifying their limitations in meeting the practical demands of TSM‐driven robotic systems. Based on experimental findings, we further propose a promising solution that simultaneously fulfills the requirements of compactness and adaptability. This work offers direct and valuable insights into the fundamental limitations of existing strategies for TSM position transmission control, whereas the proposed remedial solution demonstrates considerable potential for broad application across various TSM‐driven robotic systems.

Object detection in unmanned aerial vehicle (UAV) imagery is severely challenged by extremely small object scales, cluttered backgrounds, and pronounced foreground–background imbalance, which jointly degrade the accuracy of general-purpose detectors. This paper presents HSF-Net, a small-object detection network built upon YOLOv11s through three complementary enhancements. First, a Hierarchical Edge Enhancement Module (HEEM) employs orthogonal strip-convolution decomposition with zero-initialized residual fusion to enhance and re-weight the fine-scale edge and texture cues that are progressively attenuated in deep convolutional backbones. Second, a Sparse-Aware Feature Modulation (SAFM) module replaces concatenation-based fusion in the top-down neck pathway, coupling a sparse foreground gate with channel-wise scale modulation to confine cross-scale aggregation to object-bearing regions. Third, the detection head is restructured from {P3, P4, P5} to {P2, P3, P4}, introducing a high-resolution pathway for tiny targets while removing the original low-resolution P5 branch. On VisDrone, HSF-Net attains 46.4% mAP₅₀ and 28.5% mAP_50–95, exceeding the baseline by 7.1 and 4.8 percentage points, respectively, while reducing the parameter count from 9.4 million to 3.6 million. The model achieves an end-to-end throughput of 131.3 FPS on an NVIDIA RTX 3090, although the high-resolution P2 branch increases the computational cost to 39.5 GFLOPs. After dataset-specific training and evaluation on the markedly different TinyPerson benchmark, HSF-Net outperforms YOLOv11s by 5.2 percentage points in mAP₅₀, indicating that its relative performance advantage persists under a substantially different target-scale distribution and maritime background.