Robotics

NEURA Robotics establishes NEURA Gym RWTH Aachen to train physical AI

A rendering of the planned NEURA Gym RWTH Aachen. Source: NEURA Robotics People are not the only ones who can benefit from going to the gym. Physical AI developers are increasingly turning to specialized facilities to train models enabling robots to perceive, decide, and act in the real world. NEURA Robotics GmbH this week said it has partnered with RWTH Aachen University to address the scarcity of real-world data for training its “cognitive” robotics. “Unlike large language mo

Get smart on it

NEURA Robotics has partnered with a major research university to establish a physical training facility designed to generate real-world data for training robots. Unlike language models that train on massive volumes of data, robots have access to far less information, and simulations alone cannot capture the friction, variability, and unpredictability of actual operating environments. These facilities will combine physical training with high-fidelity simulation to create datasets that allow partners to test robots for specific uses before deploying them at full industrial scale. The company is building multiple such facilities across Europe, the U.S., and China, with plans to have half operational by the end of 2026.

The biggest AI stories, once a day

One short email a day with the stories that matter. Free. Unsubscribe with one click.

Relevant courses

Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.

Browse AI courses on Maven
Related news
RoboticsOpen story →

Google DeepMind releases new advanced robotics AI model and next-generation gene prediction model

Alphabet Inc.’s artificial intelligence research lab, Google DeepMind, Tuesday debuted the next generation of Gemini Robotics, the company’s most capable vision-language-action model to date for creating an “AI brain” for robotics. Separately, DeepMind announced AlphaGenome 2 today, a next-generation AI model that can predict how DNA sequences and their variations affect human cell types, creating […] The post Google DeepMind releases new advanced robotics AI model and next-generation gene

Also reported by MarkTechPost

RoboticsOpen story →

Gemini Robotics 2.5 accelerates real-world general dexterity

Our most capable vision-language-action model enhances robot dexterity, reliability, and safety

Also reported by Ars Technica

RoboticsOpen story →

Can AI agents program industrial robots without training data? Berkeley says yes

The new RoboSimStudio is an open-source project that enables agentic AI coding of robotic simulation environments. | Credit: UC Berkeley UC Berkeley and NVIDIA have released open-source tools for a new approach: enabling teams of AI coding agents to write, simulate, and refine readable robot programs. Since the early days of industrial robots, programming, tuning, and troubleshooting robotic workcells has taken weeks to months. New research coming out of UC Berkeley has demonstrated that new age

RoboticsOpen story →

Boston Dynamics adds wheels to Atlas humanoid to test whole-body control

Bridging the divide between deterministic physics-based control and policy-driven adaptation, Boston Dynamics has deployed a hybrid locomotion architecture on a wheeled variant of its Atlas humanoid. The “experiment” combined classical model-based motor control with whole-body reinforcement learning to maintain dynamic stability across unstructured, real-world terrain. In the video, Andy Wu, controls engineer for Atlas at Boston Dynamics, highlights some recent experiments that he

RoboticsOpen story →

Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation

Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components.  The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in Austin, Texas, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical

RoboticsOpen story →

Memories.ai builds standardized egocentric video pipeline

Memories.ai models label every clip: Memories-H0 for hand pose, EgoAction for action segments. | Credit: Memories.ai Memories.ai is building a data pipeline for robotics companies that combines standardized video collection with AI-powered annotation to turn everyday egocentric footage into training data for robots. The San Francisco-based company is developing software and AI to help robots understand the physical world. It builds video-understanding models and data infrastructure, and it colle