Browse Topic: Robotics
Extreme winter weather often leads to ice accretion on transmission lines. Manual removal is inefficient, costly, and poses safety risks. To address this issue, this paper presents the design of a de-icing robot to replace manual operations for transmission line de-icing. The main content focuses on the detailed structural design of the robot, including the mobile platform, de-icing mechanism, and adaptive adjustment module. Finite element simulations are conducted on key components to verify the structural rationality and the correctness of material selection. The proposed de-icing robot enhances the safety of the de-icing process, improves operational efficiency, and provides a valuable reference for transmission line de-icing methods, demonstrating significant practical value.
Humanoid robots have long been the focus of science fiction, but today they are making their way into industrial environments thanks to the simultaneous maturing and convergence of multiple systems. Technology advances have driven the development of humanoid robots that have a wide range of movement and can perform demanding jobs around the clock without tiring. While currently representing a small share of all industrial robot deployments, the humanoid robot market is projected to grow rapidly over the next few years. In fact, estimates suggest the market could reach over $4 billion by 2030. This growth is being driven by factors such as labor shortages, falling costs, and the need for more flexible automation.
Spinoff is NASA’s annual publication featuring successfully commercialized NASA technology. This commercialization has contributed to the development of products and services in the fields of health and medicine, consumer goods, transportation, public safety, computer technology, and environmental resources.
Physical AI refers to applications in which AI technologies are connected to hardware that sense and execute actions in the physical world, allowing systems to autonomously act and adapt in real time. It spans automotive, robotics, industrial automation, smart infrastructure, aerospace, healthcare devices, software-defined machines, and more. Regardless of application, they have one thing in common: they must operate safely, reliably, and predictably in real world environments. Unlike purely digital AI, these systems are constrained by embedded electronics, timing, power, safety, and system-level interactions that are difficult to validate early.
Robotic manipulation remains one of the harder unsolved problems in automation engineering. Vision-based systems have matured considerably — object localization, pose estimation, and grasp planning from RGB-D data are now reliable enough for structured industrial environments. What vision cannot provide is contact information: whether a grasp is stable, whether a surface is beginning to slip, or how force is distributed across a fingertip during a hold. These signals are what close the control loop during manipulation, and without them, systems compensate through excessive grip force, conservative motion profiles, and large training datasets designed to paper over sensing uncertainty.
Humanoid robots are moving beyond hype-driven prototypes toward early commercial deployment, with automotive manufacturing emerging as the first scalable adoption market, according to IDTechX’s recent report Humanoid Robots 2026-2036: Technologies, Markets, and Opportunities.
Traditional industrial robotics has been built on traditional premises: define the task precisely, program the motion, and repeat it with minimal variation. This model has delivered reliability, speed, and scale across multiple application domains.
There's a well-known video from San Francisco in 1906 that comes up repeatedly in mobility discussions here in the 21st Century. If you haven't seen A Trip Down Market Street, it depicts the absolute bonkers variety of transportation methods used on Market Street back then: cable cars, horsecars, streetcars, pedestrians, automobiles and more. Past is prologue in a world that is adding scooters, delivery robots and other last-minute delivery vehicles to our streets. At the 2026 New York International Auto Show in April, Honda displayed its latest option in the form of the Fastport eQuad Prototype. The eQuad was originally unveiled at Eurobike 2025 and technically comes from Fastport, a micromobility venture from the Honda New Business Innovation Lab that was established to work on projects with global logistics companies. Jamie Davies, chief of operations for Fastport, called the group a kind of startup within Honda. “Three years ago,” Davies told SAE Media in New York, “a small group of Honda associates [came] together and [said], Okay, how can we create a new value for the company, a new business vertical? And so we've run the project in an agile way, working with customers all along the way to understand what their needs are, what the requirements are, and to bring to market something that fits.”
Under a microscope, a bouquet of lollipop-like structures, each smaller than a grain of sand, waves gently in a petri dish of liquid. Suddenly, they snap together, like the jaws of a Venus flytrap, as a scientist waves a small magnet over the dish. What was previously an assemblage of tiny passive structures has transformed instantly into an active robotic gripper.
The future of Moon exploration may be rolling around a non-descript office on the CU Boulder campus. Here, a robot about as wide as a large pizza scoots forward on three wheels. It uses an arm with a claw at one end to pick up a plastic block from the floor, then set it back down.
Through a technology partnership that breaks new ground in the machine tool industry, Siemens offers an automation solution for the busy, multi-tasking, small to mid-sized machine shop, as it combines a digital twin of the software and programming of its popular SINUMERIK 828 CNC, working in tandem with a KUKA robot, to simplify the operation and programming in part handling for the machine tool operator.
ABB Robotics is integrating NVIDIA Omniverse libraries into ABB Robotics’ RobotStudio ® to help manufacturers deploy physical AI in real-world robotics applications.
Friendly robots, the ones people love to love, are quirky: R2-D2, C-3PO, WALL-E, BB-8, Marvin, Roz and Baymax. They’re emotional, prone to panic or bossy, empathetic and able to communicate like humans do — even when they communicate in only beeps and bloops.
Snake-like robots represent the future of rescue. Their slender bodies allow them to navigate narrow spaces, uneven terrain, and water surfaces, entering places that would be hazardous for humans. This could potentially save lives in earthquake-prone areas, like Japan.
While many manufacturers are enjoying demand for their products and have innovations in the pipeline, their growth strategy can sometimes stall. The issue might be due to workforce shortages, which can evolve from temporary setbacks to long-term challenges. For a variety of industries, helping hands are often hard to find, and it’s an issue that’s been consistent in recent years. Given this, many companies are considering transitioning to automation, including through the use of collaborative robots or cobots, for short.
Researchers in Carnegie Mellon University’s Robotics Institute (RI) have designed a system that makes an off-the-shelf quadruped robot nimble enough to walk a narrow balance beam.
Smart implants that not only stabilize a fracture but also monitor the healing process from day one — and deliver targeted support when required — are currently being developed at Saarland University by a team of engineers, medical researchers, and computer scientists. The engineering team led by Paul Motzki is contributing shape-memory micro-actuators with integrated sensing capabilities, while Bergita Ganse and her research group provide the medical expertise in fracture healing.
Picture a futuristic swarm of robots deployed on a time-sensitive task, like cleaning up an oil spill or assembling a machine. At first, adding robots is advantageous, since many hands make light work. But a tipping point comes when too many crowd the space, getting in each other’s way and slowing the whole task down.
Medical imaging technology is advancing rapidly, bringing new opportunities and challenges for machine designers. Systems that once required dedicated hospital rooms and significant floor space are becoming more compact, faster, mobile and capable of delivering increasingly detailed clinical insight. From advanced CT scanners to imaging platforms integrated with surgical robotics, imaging equipment is evolving toward more point-of-care (POC) solutions to meet rising expectations for diagnostic accuracy, procedural guidance and operational efficiency.
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm that the novel technique achieves better performance both in accuracy and computation efficiency. Also, this mixed control system can deal with complex moving path for track target object. Even when meet different obstacle and not sure measurement, it still works well with other moving obstacle in many conditions. This can be strong to face other dynamic obstacle even if have different situation with changing obstacle and uncertain data. It shows that this paper works as an attempt toward optimal solution to combine the model-based technique together with data-driven approach aiming to support real-time, highly accurate, adaptive prediction is based control technique, promising applications into industry and promoting more improved works related.
Soft robot systems demonstrate exceptional load-bearing capacity and spatial compliance during operation, with transformative potential in disaster response scenarios requiring adaptive morphology and hazardous material manipulation. By integrating the complementary advantages of soft robotics and particle jamming mechanisms, this study proposes a real-time variable-stiffness soft actuator, while systematically investigating its mathematical modeling framework and stiffness modulation principles. A deformation model for the variable stiffness soft actuator is established, followed by static analysis of the variable-stiffness members using particle jamming theory, with theoretical investigation of their stress distributions. Subsequently, a variable-stiffness driver was fabricated via additive manufacturing (3D printing), resulting in a flexible mechanical digit capable of stiffness tuning, A soft mechanical hand grasping test platform was built, and grasping experiments of objects of different shapes and sizes were conducted. Experimental validation confirms the influence of actuator dimensions, particle characteristics, and granule size distribution on both stress states and bending angles at the soft robotic digit’s distal segment. The obtained results establish theoretical foundations and advance variable-stiffness soft robotics research and associated stiffness regulation methodologies.
The global automotive industry has reached a new era. If 2025 was defined by the cautious exploration of “experimental pilots” and the collection of vast data lakes from connected vehicle fleets, 2026 marks the year that data finally gains a mind of its own within the assembly plant. We are witnessing a transition from passive automation to integrated, agentic autonomy. This is a shift that moves beyond simple programmed robotic arms and toward systems capable of independent reasoning and real-time optimization. This evolution is not just a technical upgrade; it is a fundamental restructuring of how vehicles are built, de-risked, and scaled in an increasingly volatile global economy.
Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.
In dynamic, unstructured environments like ship decks and even home kitchens, robots today still struggle to perform precision tasks such as tightening bolts or handling wires. This makes critical ship maintenance tasks difficult.
Is there a way to stick hard and soft materials together without any tape, glue, or epoxy? A new study published in ACS Central Science shows that applying a small voltage to certain objects forms chemical bonds that securely link the objects together. Reversing the direction of electron flow easily separates the two materials. This electroadhesion effect could help create biohybrid robots, improve biomedical implants, and enable new battery technologies.
A bird banking in a crosswind doesn’t rely on spinning blades. Its wings flex, twist and respond instantly to its environment.
USC Viterbi researcher received Office of Naval Research's Young Investigator Program award with Study on dexterous robotics. University of Southern California, Los Angeles, CA In dynamic, unstructured environments like ship decks and even home kitchens, robots today still struggle to perform precision tasks such as tightening bolts or handling wires. This makes critical ship maintenance tasks difficult. USC researcher, Erdem Bıyık, aims to advance robots' finger manipulation and integrate human feedback to enable real-time learning for robots in an upcoming three-year, $750,000 project funded by the Office of Naval Research (ONR).
Machina Labs recently closed its latest round of financing with $124 million, enough to develop a facility featuring up to 50 of its RoboCraftsman cells capable of producing thousands of complex structural assemblies for aerospace and defense customers - a list that already includes Lockheed Martin and the U.S. Air Force, among others. Founded in 2019, Machina Labs is a California-based company that seeks to reinvent metal manufacturing with a robot that uses artificial intelligence (AI) to rapidly form and assemble complex military grade structures directly from digital design files. RoboCraftsman is the company's manufacturing robot that leverages its proprietary “RoboForming” process to integrate multiple manufacturing processes - including metal forming, trimming, scanning, and heat treating - into a single containerized machine.
As the adoption of electric vehicles continues to accelerate, the demand for their development and testing using chassis dynamometers has also increased significantly. Compared with internal combustion engine vehicles, chassis dynamometer testing for electric vehicles typically requires test durations several to several dozen times longer, resulting in substantially increased labor requirements. In addition, low-temperature testing is often required, further intensifying the workload associated with vehicle testing. To address these challenges, this study developed and evaluated a pedal robot designed to enable unmanned and automated testing. The pedal robot developed in this study weighs only 12 kg and can be installed within a few minutes. It is, to the authors’ knowledge, the world’s first pedal robot that mimics human driving behavior by using a single foot to operate both the accelerator and brake pedals. Unlike conventional driving robots, the actuators of the proposed system do not require direct mechanical attachment to the vehicle pedals, allowing for rapid installation. Furthermore, the robot is mounted on the driver-side floor, eliminating the need for attachment to the seat structure. The pedal robot features three degrees of freedom driven by three motors and employs artificial intelligence to recognize the shape and position of pedals across different vehicle models, thereby enabling automated test initiation without manual adjustment. The performance of the pedal robot was evaluated under UDDS, HWFET, and WLTC driving modes, and the results were analyzed in accordance with the SAE J2951 standard. Comparative evaluations demonstrated that the pedal robot achieved superior speed-tracking performance relative to that of an experienced human test driver. The developed pedal robot is currently being utilized for vehicle certification testing of electric and other vehicles at the Mobile Environment Research Center of the National Institute of Environmental Research in Korea. This paper presents a detailed analysis of the corresponding experimental results.
Being an astronaut isn’t always glamorous.
Robots that can move through sand face significant challenges like dealing with higher forces than robots that move in air or water. They also get damaged more easily. However, the potential benefits of solving locomotion in sand include inspection of grain silos, measurements for soil contaminants, seafloor digging, extraterrestrial exploration, and search and rescue.
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