# VLA Articles associés

Le Centre d'actualités HTX fournit les derniers articles et analyses approfondies sur "VLA", couvrant les tendances du marché, les mises à jour des projets, les développements technologiques et les politiques réglementaires dans l'industrie crypto.

StarDynamics Secures 2.5 Billion in Two Months, State-Owned Capital Consortium Joins In

Star Era Raises 25 Billion Yuan in Two Months with State Capital Leading the Charge. Chinese humanoid robotics leader Star Era has secured a new 10-billion-yuan funding round led by state-owned capital, including funds like Chengtong Fund under the SASAC, marking 25 billion yuan raised within two months. The company, a spin-off from Tsinghua University, has built a comprehensive capital matrix combining state guidance, top-tier financial backers, and industrial partners. Founded in 2023 by Dr. Chen Jianyu, one of Tsinghua's youngest doctoral supervisors, Star Era stands out for its early and pioneering work on "world models" for embodied AI, notably releasing its PAD world action model ahead of major global players. The company follows an AI-native, full-stack R&D strategy from data and AI brain to control, dexterous hands (XHAND series), and robot bodies (bipedal L7, wheeled Q5). A core innovation is its fully direct-drive dexterous hands, which act as high-fidelity data collectors for training its AI models like the ERA-42 and VLAW, creating a virtuous cycle of data and intelligence. Star Era claims to possess one of the world's largest real-world dexterous hand datasets. Commercially, Star Era has achieved product-market fit, most notably in logistics, with robots operating 24/7 in distribution centers for partners like SF Express and China Post, handling over 1,200 parcels per hour. It is also expanding into high-end manufacturing (Samsung, Geely) and commercial services. Its hardware components are used by nine of the global top ten tech firms and leading research institutions. The article positions 2026 as an inflection point where success shifts from model capabilities to proven, scalable commercial deployment. Star Era's rapid funding and industrial traction highlight its position in this competitive race.

marsbitIl y a 10 h

StarDynamics Secures 2.5 Billion in Two Months, State-Owned Capital Consortium Joins In

marsbitIl y a 10 h

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit06/20 06:06

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit06/20 06:06

For the First Time, Pure Human Video Pretrained VLA for Dexterous Manipulation: Deployable with Minimal Fine-Tuning Data

For the first time, a purely human-video-pretrained Vision-Language-Action (VLA) model for dexterous manipulation requires only a small amount of data for fine-tuning to achieve successful real-world deployment. Achieving human-level dexterous manipulation remains a core challenge in robotics. While multi-fingered hands offer hardware potential, Visual-Language-Action (VLA) models lag behind due to the high cost of collecting diverse, high-quality robot data. A novel framework, VITRA, developed by Microsoft Research Asia and Tsinghua University, addresses this by automatically transforming massive, unlabeled real-world human activity videos into a structured V-L-A training dataset. Key innovations include precise 3D hand motion annotation from monocular video, atomic action segmentation based on hand-speed minima, and automated instruction generation using VLMs combined with 3D trajectory visualization. This process created a massive dataset of 1 million clips. Pretrained exclusively on this human video data, the VLA model (combining a VLM backbone with a Diffusion Transformer action expert) demonstrates strong zero-shot hand motion prediction in unseen environments. Crucially, it requires minimal fine-tuning (~1.2k demonstrations) on real robot data to achieve high-success-rate dexterous manipulation tasks like grasping, placing, pouring, and sweeping on hardware like the Realman robot with the XHAND1 dexterous hand. The model shows exceptional generalization to novel objects and environments. The research also observes promising scaling behavior, where performance improves with more pretraining data, paving the way for more generalized embodied intelligence.

marsbit06/08 08:54

For the First Time, Pure Human Video Pretrained VLA for Dexterous Manipulation: Deployable with Minimal Fine-Tuning Data

marsbit06/08 08:54

a16z: The Next Frontier of AI, The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

a16z presents a comprehensive investment thesis for the next frontier of AI: Physical AI, centered on a synergistic flywheel of robotics, autonomous science, and novel human-computer interfaces (HCIs) like brain-computers. While the current AI paradigm scales on language and code, the most disruptive future capabilities will emerge from three adjacent fields leveraging five core technical primitives: 1) learned representations of physical dynamics (via models like VLA, WAM, and native embodied models), 2) embodied action architectures (e.g., dual-system designs, diffusion-based motion generation, and RL fine-tuning like RECAP), 3) simulation and synthetic data as scaling infrastructure, 4) expanded sensory channels (touch, neural signals, silent speech, olfaction), and 5) closed-loop agent systems for long-horizon tasks. These primitives converge to power three key domains: * **Robotics:** The literal embodiment of AI, requiring all primitives for real-world physical interaction and manipulation. * **Autonomous Science:** Self-driving labs that conduct hypothesis-experiment-analysis loops, generating structured, causally-grounded data to improve physical AI models. * **Novel HCIs:** Devices (AR glasses, EMG wearables, BCIs) that expand human-AI bandwidth and act as massive data-collection networks for real-world human experience. These domains form a mutually reinforcing flywheel: Robotics enable autonomous labs, which in turn generate valuable data for robotics and materials science. New interfaces provide rich human-physical interaction data to train better robots and scientists. Together, they represent a new scaling axis for AI, moving beyond the digital realm to interact with and learn from physical reality, promising significant emergent capabilities and value.

marsbit04/18 07:05

a16z: The Next Frontier of AI, The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

marsbit04/18 07:05

a16z's 10,000-Word Article: The Next Frontier of AI Is Not in Language, But in the Physical World—The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

The next frontier of AI lies in the physical world, moving beyond language and code into robotics, autonomous science, and novel human-computer interfaces. These domains are powered by five core technical primitives: learned representations of dynamics, embodied action architectures, simulation and synthetic data infrastructure, expanded sensory channels, and closed-loop agent systems. Robotics applies these to real-time physical interaction, autonomous science enables AI-driven discovery through self-driving labs, and new interfaces—like AR, silent speech, and brain-computer interfaces—expand human-AI interaction bandwidth. Together, they form a mutually reinforcing flywheel: robotics enables automated science, science produces structured physical data to improve AI models, and new interfaces generate rich human-world interaction data. This convergence promises to unlock emergent capabilities as AI begins to scale in the physical domain.

marsbit04/16 06:51

a16z's 10,000-Word Article: The Next Frontier of AI Is Not in Language, But in the Physical World—The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

marsbit04/16 06:51

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