Flexible tactile sensors are increasingly used in real-world applications such as robotic grippers, prosthetic hands, wearable gloves, and assistive devices, where they need to conform to curved and irregular surfaces. However, most existing tactile sensors are calibrated only on flat substrates, and their accuracy and consistency degrade once mounted on curved geometries. This limitation restricts their reliability in practical use. To address this challenge, we develop a calibration model for a widely used resistive tactile sensor design that enables accurate force estimation on one-dimensional curved surfaces. We then train a neural network (a multilayer perceptron) to predict local curvature from baseline sensor outputs recorded under no applied load, achieving an R2 score of 0.91. The proposed approach is validated on five daily objects with varying curvatures under forces from 2 N to 8 N. Results show that the curvature-aware calibration maintains consistent force accuracy across all surfaces, while flat-surface calibration underestimates force as curvature increases. Our results demonstrate that curvature-aware modeling improves the accuracy, consistency, and reliability of flexible tactile sensors, enabling dependable performance across real-world applications.

We have also shown scaling of the developmental algorithm with the network size in supplementary (S5).

In recent years, parametric representations of point clouds have been widely applied in tasks such as memory-efficient mapping and multi-robot collaboration. Highly adaptive models, like spline surfaces or quadrics, are computationally expensive in detection or fitting. In contrast, real-time methods, such as Gaussian mixture models or planes, have low degrees of freedom, making high accuracy with few primitives difficult. To tackle this problem, a multi-model parametric representation with real-time surface detection and fitting is proposed. Specifically, the Gaussian mixture model is first employed to segment the point cloud into multiple clusters. Then, flat clusters are selected and merged into planes or curved surfaces. Planes can be easily fitted and delimited by a 2D voxel-based boundary description method. Surfaces with curvature are fitted by B-spline surfaces and the same boundary description method is employed. Through evaluations on multiple public datasets, the proposed surface detection exhibits greater robustness than the state-of-the-art approach, with 3.78 times improvement in efficiency. Meanwhile, this representation achieves a 2-fold gain in accuracy over Gaussian mixture models, operating at 36.4 fps on a low-power onboard computer.

Synthetic active collectives, composed of many nonliving individuals capable of cooperative changes in group shape and dynamics, hold promise for practical applications and for the elucidation of guiding principles of natural collectives. However, the design of collective robotic systems that operate effectively without intelligence or complex control at either the individual or group level is challenging. We investigate how simple steric interaction constraints between active individuals produce a versatile active system with promising functionality. Here we introduce the link-bot: a V-shape-based, single-stranded chain composed of active bots whose dynamics are defined by its geometric link constraints, allowing it to possess scale- and processing-free programmable collective behaviors. A variety of emergent properties arise from this dynamic system, including locomotion, navigation, transportation, and competitive or cooperative interactions. Through the control of a few link parameters, link-bots show rich usefulness by performing a variety of divergent tasks, including traversing or obstructing narrow spaces, passing by or enclosing objects, and propelling loads in both forward and backward directions. The reconfigurable nature of the link-bot suggests that our approach may significantly contribute to the development of programmable soft robotic systems with minimal information and materials at any scale.

Skateboarders call it "pumping," and it's a skill that both Olympic medalists and aspiring thrashers use to build launch speed from what seems like thin air. But what separates the steeziest pro from the sketchiest beginner is the years' worth of practice it takes to develop the know-how to execute the cleanest pump -- or at least that was the case until now. In a paper published Monday in the journal Physical Review Research, scientists have revealed the secret of achieving serious airtime. A skateboarder rides the bowls at Etnies skatepark in Lake Forest. With a bit of coding, researchers were able to describe the optimal technique for pumping -- a tactic where skateboarders crouch down low momentarily and then push their body upright on inclines.

Tactile sensing is essential for robots to perceive and react to the environment. However, it remains a challenge to make large-scale and flexible tactile skins on robots. Industrial machine knitting provides solutions to manufacture customizable fabrics. Along with functional yarns, it can produce highly customizable circuits that can be made into tactile skins for robots. In this work, we present RobotSweater, a machine-knitted pressure-sensitive tactile skin that can be easily applied on robots. We design and fabricate a parameterized multi-layer tactile skin using off-the-shelf yarns, and characterize our sensor on both a flat testbed and a curved surface to show its robust contact detection, multi-contact localization, and pressure sensing capabilities. The sensor is fabricated using a well-established textile manufacturing process with a programmable industrial knitting machine, which makes it highly customizable and low-cost. The textile nature of the sensor also makes it easily fit curved surfaces of different robots and have a friendly appearance. Using our tactile skins, we conduct closed-loop control with tactile feedback for two applications: (1) human lead-through control of a robot arm, and (2) human-robot interaction with a mobile robot.

Yes, you heard that right. Researchers at the Korea Advanced Institute of Science and Technology have developed a four-legged robot that can climb up iron and steel walls and ceilings, as described a study published in the journal Science Robotics on Wednesday. They call it MARVEL, short for Magnetically Adhesive Robot for Versatile and Expeditious Locomotion, and it only weighs about 18 pounds and isn't any larger than a tiny puppy at roughly 13 in long. MARVEL isn't the first robot that can climb walls, but unlike most others, it makes use of magnetic legs rather than wheels, grippers, suction cups, or propellers. It's also seriously dexterous, its designers say, adroitly navigating curved surfaces like that of a rusted metal storage tank, in part thanks to its innovative feet that use electro-magnets and a cutting edge, rubber-like smart material known as magnetorheological elastomers.

Drilling a hole on a curved surface with a desired angle is prone to failure when done manually, due to the difficulties in drill alignment and also inherent instabilities of the task, potentially causing injury and fatigue to the workers. On the other hand, it can be impractical to fully automate such a task in real manufacturing environments because the parts arriving at an assembly line can have various complex shapes where drill point locations are not easily accessible, making automated path planning difficult. In this work, an adaptive admittance controller with 6 degrees of freedom is developed and deployed on a KUKA LBR iiwa 7 cobot such that the operator is able to manipulate a drill mounted on the robot with one hand comfortably and open holes on a curved surface with haptic guidance of the cobot and visual guidance provided through an AR interface. Real-time adaptation of the admittance damping provides more transparency when driving the robot in free space while ensuring stability during drilling. After the user brings the drill sufficiently close to the drill target and roughly aligns to the desired drilling angle, the haptic guidance module fine tunes the alignment first and then constrains the user movement to the drilling axis only, after which the operator simply pushes the drill into the workpiece with minimal effort. Two sets of experiments were conducted to investigate the potential benefits of the haptic guidance module quantitatively (Experiment I) and also the practical value of the proposed pHRI system for real manufacturing settings based on the subjective opinion of the participants (Experiment II).

Daewoo Shipbuilding & Marine Engineering is the first global shipbuilding industry with artificial intelligence for hot processing A robot system that combines technology is applied. Daewoo Shipbuilding & Marine Engineering (CEO Lee Seong-Geun) has developed an artificial intelligent hot processing robot'Goknuri' that can produce high-quality products even with low-skilled people using standardized big data and artificial intelligence technology while improving the working environment and applied it to the field It was revealed on the 20th. The newly developed robot'Goknuri' contributes to maintaining high quality by standardizing work contents while storing and utilizing the know-how and performance of existing workers as data. In addition, the accumulated data can be used for the construction of other ships using artificial intelligence technology in the future. In addition, it is possible to dramatically improve the working environment of workers who have been exposed to noise and musculoskeletal diseases.