ALOHA2 is an enhanced version of the dual-arm teleoperated robot ALOHA [1], featuring higher performance and robustness compared to the original design [2], while also being more ergonomic. Like ALOHA, ALOHA2 consists of two grippers and two ViperX 6-DoF arms, as well as two smaller WidowX arms. Users control the follower mechanical arms by operating the leader mechanical arms through back-driving. The device also includes cameras that generate images from multiple viewpoints, allowing for RGB data collection during teleoperation. The robot is mounted on a 48-inch 30-inch table, equipped with an aluminum frame that provides additional mounting points for cameras and gravity compensation systems (as shown in Figure 1).

It turns out that Roborock isn't the only company that brought a robot vacuum with a mechanical arm to CES 2025. Rival company Dreame, which unveiled its stair-climbing robot vacuum earlier in the week, is also working on a robot vacuum with an arm for picking up objects. The device is still a prototype, according to the company, but the as yet unnamed robo vac was on full display at Dreame's CES booth. Considering it's still a prototype, the actual arm looked far more substantial compared to the one on Roborock's Saros Z70. It was much thicker and had a bigger "claw" that looked like it might be able to pick up slightly heavier objects.

Language-conditioned robot models (i.e., robotic foundation models) enable robots to perform a wide range of tasks based on natural language instructions. Despite strong performance on existing benchmarks, evaluating the safety and effectiveness of these models is challenging due to the complexity of testing all possible language variations. Current benchmarks have two key limitations: they rely on a limited set of human-generated instructions, missing many challenging cases, and they focus only on task performance without assessing safety, such as avoiding damage. To address these gaps, we introduce Embodied Red Teaming (ERT), a new evaluation method that generates diverse and challenging instructions to test these models. ERT uses automated red teaming techniques with Vision Language Models (VLMs) to create contextually grounded, difficult instructions. Experimental results show that state-of-the-art models frequently fail or behave unsafely on ERT tests, underscoring the shortcomings of current benchmarks in evaluating real-world performance and safety. Code and videos are available at: https://sites.google.com/view/embodiedredteam.

Safety concerns have risen as robots become more integrated into our daily lives and continue to interact closely with humans. One of the most crucial safety priorities is preventing collisions between robots and people walking nearby. Despite developing various algorithms to address this issue, evaluating their effectiveness on a cost-effective test bench remains a significant challenge. In this work, we propose a solution by introducing a simple yet functional platform that enables researchers and developers to assess how humans interact with mobile robots. This platform is designed to provide a quick yet accurate evaluation of the performance of safe interaction algorithms and make informed decisions for future development. The platform's features and structure are detailed, along with the initial testing results using two preliminary algorithms. The results obtained from the evaluation were consistent with theoretical calculations, demonstrating its effectiveness in assessing human-robot interaction. Our solution provides a preliminary yet reliable approach to ensure the safety of both robots and humans in their daily interactions.

At the Boulder County Recycling Center in Colorado, two team members spend all day pulling items from a conveyor belt covered in junk collected from the area's bins. One plucks out juice cartons and plastic bottles that can be reprocessed, while the other searches for contaminants in the stream of paper products headed to a fiber mill. They are Sorty McSortface and Sir Sorts-a-Lot, AI-powered robots that each resemble a supercharged mechanical arm from an arcade claw machine. Developed by the tech start-up Amp Robotics, McSortface and Sorts-a-Lot's appendages dart down with the speed of long-beaked cranes picking fish out of the water, suctioning up items they've been trained to recognize. Yes, even recycling has gotten tangled up in the AI revolution. Amp Robotics has its tech in nearly 80 facilities across the U.S., according to a company spokesperson, and in recent years, AI-powered sorting from companies such as Bulk Handling Systems and MachineX has popped up in other recycling plants.

NaaS Technology one of the largest and fastest growing electric vehicle charging service providers in China, recently announced the launch of its first self-developed automatic charging robot with features including active vehicle locating, smart charging, and automatic payment settlement to meet the rising demand for mobile charging of electric vehicles ("EV"). With the future popularization of self-driving vehicles, compatible automatic charging robots will become indispensable infrastructure. During an automatic charging session, robots with mechanical arms will automatically dock with the ports of EVs and complete the charging and settlement processes in one go. Empowered by deep learning, 5G, V2X, simultaneous localization and mapping, and other underlying technologies, NaaS' waterproof and shock-proof charging robot brings science fiction to life with one-click ordering, active vehicle locating, precise self-parking, automatic docking, charging and undocking via mechanical arms, and automatic return and recharging functions. It is available in various charging power and battery capacity configurations and can connect with major OEMs seamlessly through an open API, enabling EV owners to enjoy unmanned service anywhere, around the clock, saving much time and effort.

This article was published as a part of the Data Science Blogathon. Artificial Intelligence (AI) has made a great impact across a wide range of industries, especially healthcare. This blog covers some of the applications of AI in the healthcare sector. Many hospitals use robotic technology to help them complete tasks that call for accuracy, control, and flexibility. It is used for very complex tasks requiring excellent skills and accuracy.

Robots of the future could learn to grasp and pick up delicate objects thanks to a new material inspired by human skin. Experts have built a tactile sensor that detects pressure and sends out an electric pulse in response to touch. As well as its applications for intelligent machines, the breakthrough could lead to prosthetic limbs that let people with disabilities feel again. Robots of the future could learn to grasp and pick up delicate objects thanks to a new material inspired by human skin. Experts have built a tactile sensor that detects pressure.

Last Sunday, a particularly unusual DotA 2 tournament took place. DotA, a complicated, real-time strategy game, is among the most popular e-sports in the world. The five players of one team--Blitz, Cap, Fogged, Merlini, and MoonMeander--were ranked in the 99.95th percentile, inarguably among the best DotA 2 players in the world. However, their opponent still defeated them in two out three games, winning the tournament. An evenly matched game is supposed to take 45 minutes, but these two were over in 14 and 21 minutes, respectively. Their opponent was a team of five neural networks developed by Elon Musk's OpenAI, collectively referred to as OpenAI Five.

George C. Devol, a largely self-taught inventor who drew from science fiction to help develop Unimate, the revolutionary mechanical arm that became a prototype for robots now widely used on automobile assembly lines and in other industries, died on Thursday at his home in Wilton, Conn. In the early 1950s, before the advent of industrial robotics, Mr. Devol (pronounced de-VAHL) built on his own work in electrical engineering and machine controls to design a mechanical arm that could be programmed to repeat precise tasks, like grasping and lifting. He applied for a patent in 1954 and explained the concept to a fellow engineer, Joseph F. Engelberger, at a cocktail party where they discussed their favorite science fiction writers. Mr. Engelberger listened with interest and immediately seized on the significance of the new technology. Mr. Devol named the concept Universal Automation -- later shortened to Unimation -- and received a patent in 1961.