
ENvue Medical develops robotic feeding tube placement system
ENvue Drive demonstrates insertion of a feeding tube into a patient. | Source: ENvue Medical ENvue Medical Inc. recently unveiled ENvue Drive, its robotic-assisted tool for automatically placing feeding tubes. The tool is built on the company’s electromagnetic navigation platform ecosystem, and is designed to integrate artificial intelligence, real-time navigation, and robotic-assisted procedural execution into a unified clinical workflow. Each year, over a million feeding tubes are insert
A company has developed a robotic system for placing feeding tubes, which are tubes inserted into patients to deliver nutrition. The system uses electromagnetic navigation with artificial intelligence and sensors to guide the tube placement, addressing a significant problem: approximately 3 to 5 percent of feeding tubes are currently misplaced and enter the lungs instead, which can cause serious complications. Over a million feeding tubes are placed annually in U.S. hospitals across critical care, adult, and pediatric patients, making safer and more reliable placement methods important. The robotic system is designed to assist clinicians rather than replace them, with the clinician maintaining control throughout the procedure while receiving real-time guidance on screen.
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Browse AI courses on MavenGoogle DeepMind releases new advanced robotics AI model and next-generation gene prediction model
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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