Agility Robotics reports $1.8M revenue ahead of humanoid SPAC
Agility Robotics said it has deployed its Digit humanoid at 9 customer sites as it gears up to go public via a proposed SPAC merger with Churchill Capital Corp. XI. Now we’re getting a first look at Agility’s financials thanks to a new S-4 filing with the SEC. According to the S-4, Agility generated $1.8 million in net sales in 2025, against a $140 million operating loss. The company spent $111 million on operations, up from $71 million in 2024. Agility burned about $100 million in cash in 20
Agility Robotics, a humanoid robot maker, reported $1.8 million in revenue for 2025 while operating at a $140 million loss, according to financial documents filed ahead of its planned merger with a SPAC that would value the company at $2.5 billion. The company has deployed its Digit humanoid robot at 9 customer sites and has received more than $300 million in multi-year orders from one customer, though these deployments have not yet generated substantial revenue relative to the company's valuation. Agility plans to generate revenue through two models: a robot-as-a-service subscription at $8,500 monthly or direct sales, and projects deploying thousands of robots by 2030 to reach billions in annual revenue. The wide gap between current revenue and valuation reflects how SPAC mergers allow private companies to go public based on future projections rather than demonstrated financial performance.
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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
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
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