
Vention opens Physical AI Lab for manufacturing in Montreal
Vention and its partners have invested in space and equipment for its new physical AI lab. Source: Vention To generate high-quality data for training the AI models that operate next-generation robots, you need scale. Vention Inc. today opened its Physical AI Lab in Montreal. The company said the new laboratory focuses on robotic manipulation in manufacturing. “Any physical AI foundation model is data-hungry,” Etienne Lacroix, founder and CEO of Vention, told The Robot Report. “
A manufacturing automation company opened a laboratory focused on training AI models for robotic tasks in factories. The lab leverages data collected from thousands of deployed machines worldwide to develop and test physical AI capabilities in real production environments rather than just laboratory settings. Physical AI requires large amounts of high-quality industrial data to train models effectively, and the company's scale of deployment gives it access to continuous real-world information from manufacturing operations. The laboratory combines academic research with direct feedback from active factory floors to move AI advances from research concepts into reliable, cost-effective automation tools that manufacturers can actually deploy.
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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

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

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

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