High-performance closed-loop control of truly soft continuum manipulators has remained elusive. Experimental demonstrations have largely relied on sufficiently stiff, piecewise architectures in which each actuated segment behaves as a distributed yet effectively rigid element, while deformation modes beyond simple bending are suppressed. This strategy simplifies modeling and control, but sidesteps the intrinsic complexity of a fully compliant body and makes the system behave as a serial kinematic chain, much like a conventional articulated robot. An implicit conclusion has consequently emerged within the community: distributed softness and dynamic precision are incompatible. Here we show this trade-off is not fundamental. We present a highly compliant, fully continuum robotic arm - without hardware discretization or stiffness-based mode suppression - that achieves fast, precise task-space convergence under dynamic conditions. The platform integrates direct-drive actuation, a tendon routing scheme enabling coupled bending and twisting, and a structured nonlinear control architecture grounded in reduced-order strain modeling of underactuated systems. Modeling, actuation, and control are co-designed to preserve essential mechanical complexity while enabling high-bandwidth loop closure. Experiments demonstrate accurate, repeatable execution of dynamic Cartesian tasks, including fast positioning and interaction. The proposed system achieves the fastest reported task-execution speed among soft robots. At millimetric precision, execution speed increases nearly fourfold compared with prior approaches, while operating on a fully compliant continuum body. These results show that distributed compliance and high-performance dynamic control can coexist, opening a path toward truly soft manipulators approaching the operational capabilities of rigid robots without sacrificing morphological richness.
Shenzhen Key Lab of Advanced Motion Control Technology and Modern Automation Equipment（深圳先进运动控制技术及现代自动化装备重点实验室）；School of Intelligence Science and Engineering（智能科学与工程学院）；College of Artificial Intelligence（人工智能学院）；Harbin Institute of Technology（哈尔滨工业大学）；School of Mechanical and Aerospace Engineering（机械与航空航天工程学院）；Nanyang Technological University（南洋理工大学）；School of Robotics and Automation（机器人与自动化学院）；Nanjing University（南京大学）；School of Advanced Engineering（先进工程学院）；Great Bay University（大贝大学）；Center for Precision Engineering（精密工程中心）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
A Novel Modular Cable-Driven Soft Robotic Arm with Multi-Segment Reconfigurability
Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密苏里州立大学）；Department of Electrical and Computer Engineering, Michigan State University（电气与计算机工程系，密苏里州立大学）；Texas A&M AgriLife Research and Extension Center in Dallas, Texas A&M University（德克萨斯A&M大学达拉斯分校农业与生命科学研究中心）
CATCH-FORM-3D: Compliance-Aware Tactile Control and Hybrid Deformation Regulation for 3D Viscoelastic Object Manipulation
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；Institute for Super Robotics (Huangpu)（（黄埔）超级机器人研究所）
Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors
L3S Research Center , Leibniz Universität Hannover（L3S研究所以及汉诺威莱布尼茨大学）；Department of Biomedical Engineering and the School of Brain Sciences, Ben-Gurion University of the Negev（生物医学工程系和脑科学学院，本·古里安大学）
Visual Pressure Estimation and Control for Soft Robotic Grippers
Georgia Institute of Technology（佐治亚理工学院）；Intel Labs（英特尔研究院）；Meta Reality Labs（Meta现实实验室）；Hello Robot Inc.（Hello Robot公司）
柔软的机器人抓手有助于实现接触式操作，包括对各种物体的牢固抓取。然而，软夹持器的有利顺应性也导致显著变形，这可能使精确操作具有挑战性。我们提出了视觉压力估计和控制（VPEC），一种方法，推断软抓手使用RGB图像从外部摄像头施加的压力。我们提供的视觉压力推断的结果时，气动夹持器和肌腱致动的夹持器与平面接触。我们还表明，VPEC可以通过推断的压力图像的闭环控制进行精确操作。在我们的评估中，移动机械手（来自Hello Robot的Stretch RE 1）使用视觉伺服在所需的压力下进行接触;遵循空间压力轨迹;并抓住小的低轮廓物体，包括microSD卡，硬币和药丸。总的来说，我们的研究结果表明，视觉估计施加的压力可以使软夹持器进行精确的操作。
A Biomimetic Vertebraic Soft Robotic Tail for High-Speed, High-Force Dynamic Maneuvering
Shenzhen Technology University（深圳技术大学）；Southern University of Science and Technology（南方科技大学）；Great Bay University（大湾区大学）；Tencent Robotics X（腾讯机器人X）；Wisson Robotics（威世机器人）；asRobotics
Hybrid Robotic Meta-gripper for Tomato Harvesting: Analysis of Auxetic Structures with Lattice Orientation Variations
Tohoku University, Japan（东京东京大学）；Delft University of Technology (TU Delft), the Netherlands（代尔夫特理工大学）；Indian Institute of Technology Kanpur (IIT Kanpur), India（印度理工学院坎浦尔分校）
Miniaturized Circuitry for Capacitive Self-sensing and Closed-loop Control of Soft Electrostatic Transducers
University of Colorado（科罗拉多大学）；Materials Science and Engineering Program, University of Colorado（科罗拉多大学材料科学与工程项目）；Department of Electrical Engineering, University of Colorado（科罗拉多大学电气工程系）；Department of Computer Science, University of Colorado（科罗拉多大学计算机科学系）
External Photoreflective Tactile Sensing Based on Surface Deformation Measurement
Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）；Department of Mechanical Engineering and Science, Kyoto University（京都大学机械工学科）；Faculty of Design Department of Media Design, Kyushu University（九州大学设计学部媒体设计学科）
GelSight Baby Fin Ray: A Compact, Compliant, Flexible Finger with High-Resolution Tactile Sensing
Massachusetts Institute of Technology（麻省理工学院）
触觉感知与合规性的结合对于许多领域都至关重要，从水果采摘等农业用途，到分类回收等可持续性实践，再到为老年人创造安全的家庭护理机器人，让他们有尊严地度过晚年。通过触觉感知，我们可以辨别材料属性，识别纹理，并确定柔软度，同时通过顺应性，我们能够安全地与周围的物体和环境进行交互。这两种能力可以最终成为一个有用的软机器人抓手，例如最初的GelSight Fin Ray，它能够抓住各种不同的物体，也可以执行简单的家庭操作任务：酒杯重新定位。虽然最初的GelSight Fin Ray解决了将一般刚性的高分辨率传感器与柔软的顺应性结构连接的问题，但我们可以提高传感器的鲁棒性并实施技术，使这种基于摄像头的触觉传感器适用于更广泛的软体机器人设计。我们首先集成了柔性反射镜，并将刚性电子元件集成到夹持器的基座中，这大大提高了Fin Ray结构的顺应性。然后，我们合成了一种柔性和高伸长率的有机硅粘合剂基荧光涂料，它可以为我们的传感器提供良好质量的二维触觉定位结果。最后，我们将所有这些技术整合到一个新的设计中：Baby Fin Ray，我们用它来挖掘杂物，并成功地对坚果进行分类。补充视频可以在这里找到：https://youtu.be/_oD_QFtYTPM
