Robotics

Founder’s cost-cutting obsession drove Unitree lead in cheap humanoid robots

China leads the world in churning out humanoid robots and four-legged robot dogs that also happen to be the most affordable on the market—and Unitree Robotics’ founder Wang Xingxing is arguably one of the people most responsible for that Chinese lead. The introverted founder, who prominently appeared at a 2025 business symposium hosted by Chinese President Xi Jinping, became phenomenally wealthy after Unitree launched an initial public offering on the Shanghai Stock Exchange STAR Market on Augus

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China's leading humanoid robot manufacturer has achieved low prices through its founder's extreme attention to cost-cutting and micromanagement of nearly every design detail, from screw lengths to material colors. The founder's hands-on leadership style, which involves personally approving expenses over a small threshold and working around the clock, helped the company become profitable and launch a successful stock offering, but employees report high staff turnover and a penalty-based incentive system that may not scale as the company grows. The company faces challenges including quality control issues in earlier years, dependence on university and research institution customers rather than industrial users, and a US government ban on importing its robots that has disrupted distribution. Broader questions remain about whether humanoid robots have genuine commercial viability beyond research applications.

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Gemini Robotics 2.5 accelerates real-world general dexterity

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

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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

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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

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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

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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