
AGIBOT unveils four embodied AI products for real-world operations at WAIC
The AGIBOT G2 wheeled robot provided subway guidance at WAIC 2026. Source: AGIBOT Embodied AI including humanoid robots are moving toward wider production and deployment. AGIBOT yesterday unveiled four new products at the World Artificial Intelligence Conference, or WAIC. The company said they expand its portfolio across full-size humanoid platforms, heavy-payload industrial task robots, and direct-drive dexterous manipulation systems. “The industry should no longer judge embodied AI by whether
A robotics company unveiled four new embodied AI products designed for real-world commercial and industrial use rather than one-time demonstrations. The products include a full-size humanoid robot for service environments, a heavy-payload industrial task robot for material handling, a dexterous robotic hand for complex manipulation, and an educational platform. The company emphasized that the industry should judge embodied AI by whether robots can be reliably manufactured, delivered, and integrated into actual operating environments, and presented factory deployments where their robots have performed repetitive tasks continuously over extended periods.
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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