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Austin-based robotics startup Apptronik has unveiled Apollo 2, the latest version of its humanoid robot. Apollo 2 comes in both bipedal and wheeled versions and performs real-world tasks while collecting training data. Apptronik is using this data with Google DeepMind to train Gemini Robotics AI models, making Apollo smarter...

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Apptronik is in advanced talks to secure a new funding round of at least $400 million, led by existing investor B Capital, potentially reaching a $5 billion pre-money valuation. This follows a previous Series A round in February 2025 where the Austin-based humanoid robotics company raised $350 million, co-led by B Capital and Capital Factory, with participation from Google. Apptronik is also in a strategic partnership with Google DeepMind to integrate advanced AI models. Here’s Apptronik’s Apollo robot showcasing Gemini Robotics 1.5’s agentic capabilities.
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Apptronik is in advanced talks to secure a new funding round of at least $400 million, led by existing investor B Capital, potentially reaching a $5 billion pre-money valuation. This follows a previous Series A round in February 2025 where the Austin-based humanoid robotics company raised $350 million, co-led by B Capital and Capital Factory, with participation from Google. Apptronik is also in a strategic partnership with Google DeepMind to integrate advanced AI models. Here’s Apptronik’s Apollo robot showcasing Gemini Robotics 1.5’s agentic capabilities.

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We're thrilled to partner with GXO, the world's largest pure-play contract logistics provider, to advance humanoid robotics in the logistics industry! 🤝🤖📦 Our multi-phase R&D initiative with Apollo aims to tackle labor-intensive tasks, improve safety, and enhance efficiency. Stay tuned for updates as we work together to achieve logistics at full potential! Learn more: #apptronik #gxo
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We're thrilled to partner with GXO, the world's largest pure-play contract logistics provider, to advance humanoid robotics in the logistics industry! 🤝🤖📦 Our multi-phase R&D initiative with Apollo aims to tackle labor-intensive tasks, improve safety, and enhance efficiency. Stay tuned for updates as we work together to achieve logistics at full potential! Learn more: #apptronik #gxo

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Most robotics AI models suffer from the "stop-and-think" problem. They take a static picture, pause to reason, execute an action, and repeat. In the real world, that latency causes spills, collisions, and failed tasks. Google DeepMind just launched Gemini Robotics ER 2: an embodied reasoning model that thinks and acts at the speed of the physical world. Here's why this is a step-change for physical AI engineering: Traditional robotics models rely on static snapshots. But knowing *when* a task is done, such as when to stop pouring coffee into a cup or when a trash bag is securely tied, requires continuous temporal awareness. Gemini Robotics ER 2 integrates directly with the bidirectional streaming Gemini Live API to reason about what comes next while simultaneously executing motor actions. What makes Gemini Robotics ER 2 different: 🎯 91.3% accuracy on live video moment-finding (0.96s mean absolute distance) at 4x the execution speed of frontier models 📈 Continuous progress tracking across 5 completion stages (57.4% accuracy) to self-correct mid-task without restarting 🛠️ Native agentic tool orchestration that commands lower-level VLA models, navigation APIs, and Google Search 🤝 Multi-robot collaboration allowing physically diverse machines (like Apptronik's Apollo 2 humanoid and Franka's FR3 Duo arm) to hand off tasks in shared spaces 🛡️ Built-in physical safety that autonomously halts robots when humans enter a workspace and resumes once clear

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Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.

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Sundar Pichai on the robotics opportunity: ⦿ He's impressed by recent humanoid progress - so much so that he sometimes has to look closely to tell if a robot video is real or fake. ⦿ Google initially moved too early into the application layer, but now sees the combination of AI and robotics as the “sweet spot.” ⦿ He believes we’re 2–3 years away from a “magical moment” in robotics. ⦿ Google is working on playing a role similar to Android in robotics - one of its internal bets is already focused on supporting robotics manufacturers. ⦿ Gemini robotics models will work very well - how to productize them is still an open question at the company.

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New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact
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New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact

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Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

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