Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry’s appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery.

Developing robotic hands that adapt to real-world dynamics remains a fundamental challenge in robotics and machine intelligence. Despite notable advances in replicating human-hand kinematics and control algorithms, robotic systems still struggle to match human capabilities in dynamic environments, primarily due to inadequate tactile feedback. To bridge this gap, we present F-TAC Hand, a biomimetic hand featuring high-resolution tactile sensing (0.1-mm spatial resolution) across 70% of its surface area. Through optimized hand design, we overcome traditional challenges in integrating high-resolution tactile sensors while preserving the full range of motion. The hand, powered by our generative algorithm that synthesizes human-like hand configurations, demonstrates robust grasping capabilities in dynamic real-world conditions. Extensive evaluation across 600 real-world trials demonstrates that this tactile-embodied system significantly outperforms non-tactile-informed alternatives in complex manipulation tasks (P < 0.0001). These results provide empirical evidence for the critical role of rich tactile embodiment in developing advanced robotic intelligence, offering promising perspectives on the relationship between physical sensing capabilities and intelligent behaviour.

Continuum manipulators are a rapidly emerging class of robots. However, due to the complexity of their mathematical models and modeling inaccuracies, the development of effective control systems is a particularly challenging task. This paper presents the first attempt on kinematic control of continuum manipulators using a fuzzy-model-based approach. A fuzzy controller is proposed for autonomous execution of end-effector trajectory tracking tasks for a continuum manipulator. Particularly, membership functions are employed to combine the linearized state-space models, to achieve, overall, a fuzzy model. The fuzzy model can help design the fuzzy controller; in our approach, this process is supported by a thorough stability analysis. This control methodology enables a solution with low computational requirements to this motion control problem - there is no need to continuously update the Jacobian of the continuum manipulator. The superior performance of this controller is validated in MATLAB simulations and compared with those of classical controllers found in the literature. The experiments on a rapid-prototyped continuum manipulator further verify the feasibility and the advantages of this fuzzy controller in the presence of modeling discrepancies and hardware inaccuracies.

This paper presents a multi-axis force/torque sensor based on simply-supported beam and optoelectronic technology. The sensor’s main advantages are: (1) Low power consumption; (2) low-level noise in comparison with conventional methods of force sensing (e.g., using strain gauges); (3) the ability to be embedded into different mechanical structures; (4) miniaturisation; (5) simple manufacture and customisation to fit a wide-range of robot systems; and (6) low-cost fabrication and assembly of sensor structure. For these reasons, the proposed multi-axis force/torque sensor can be used in a wide range of application areas including medical robotics, manufacturing, and areas involving human–robot interaction. This paper shows the application of our concept of a force/torque sensor to flexible continuum manipulators: A cylindrical MIS (Minimally Invasive Surgery) robot, and includes its design, fabrication, and evaluation tests.

Dealing safely with nuclear waste is an imperative for the nuclear industry. Increasingly, robots are being developed to carry out complex tasks such as perceiving, grasping, cutting, and manipulating waste. Radioactive material can be sorted, and either stored safely or disposed of appropriately, entirely through the actions of remotely controlled robots. Radiological characterisation is also critical during the decommissioning of nuclear facilities. It involves the detection and labelling of radiation levels, waste materials, and contaminants, as well as determining other related parameters (e.g., thermal and chemical), with the data visualised as 3D scene models. This paper overviews work by researchers at the QMUL Centre for Advanced Robotics (ARQ), a partner in the UK EPSRC National Centre for Nuclear Robotics (NCNR), a consortium working on the development of radiation-hardened robots fit to handle nuclear waste. Three areas of nuclear-related research are covered here: human–robot interfaces for remote operations, sensor delivery, and intelligent robotic manipulation.

Plant-inspired inflatable eversion robots with their tip growing behaviour have recently emerged. Because they extend from the tip, eversion robots are particularly suitable for applications that require reaching into remote places through narrow openings. Besides, they can vary their structural stiffness. Despite these essential properties which make the eversion robot a promising candidate for applications involving cluttered environments and tight spaces, controlling their motion especially laterally has not been investigated in depth. In this paper, we present a new approach based on model-based kinematics to control the eversion robot's tip position and orientation. Our control approach is based on Euler-Bernoulli beam theory which takes into account the effect of the internal inflation pressure to model each robot bending segment for various conditions of structural stiffness. We determined the parameters of our bending model by performing a least-square technique based on the pressure-bending data acquired from an experimental study. The model is then used to develop a pose controller for the tip of our eversion robot. Experimental results show that the proposed control strategy is capable of guiding the tip of the eversion robot to reach a desired position and orientation whilst varying its structural stiffness.