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#Skild AI

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2026-10-02Fri
  1. Skild AI — Blog62

    Building the general-purpose robotic brain

    Skild AI introduces their omni-bodied robotics foundation model, the Skild Brain, which is trained across diverse morphologies and data sources to enable generalization across tasks and hardware. The model uses a hierarchical architecture with low- and high-frequency policies, and is pre-trained using large-scale simulation and internet video data, with post-training on real-world data. This model represents a significant step toward creating a general-purpose robotic brain capable of operating on various robot types.

    Editorial context：Skild AI introduces their omni-bodied robotics foundation model, the Skild Brain, which is trained across diverse morphologies and data sources to enable generalization across tasks and hardware. The model uses a hierarchical architecture with low- and high-frequency policies, and is pre-trained using large-scale simulation and internet video data, with post-training on real-world data. This model

  2. Skild AI — Blog85

    The case for an omni-bodied robot brain

    Skild AI presents a research paper detailing the development of an AI model trained across a vast array of robot bodies, enabling it to adapt to unpredictable scenarios without prior exposure. The model demonstrates zero-shot control and in-context learning, showing resilience in scenarios like limb loss, joint failure, and morphological changes. The work highlights the importance of adaptability in embodied AI for real-world applications.

    Editorial context：This research introduces an 'omni-bodied' AI model trained across 100,000 different robot bodies, demonstrating zero-shot adaptation to extreme morphological changes through in-context learning. The approach emphasizes the need for AI to adapt rather than memorize, drawing parallels to biological evolution and AGI development.

2026-09-30Wed
  1. RoboSpeak — WeChat86

    Humanoid Robot Patents: China Claims 60% Share, What Next?

    The article analyzes China's leading position in the field of humanoid robot patents, noting that as of the first half of 2026, China holds 60.8% of relevant patents, significantly surpassing the United States and South Korea. The article examines the evolution of patent strategies, the trend of technological shift from hardware to software, and the progress made by Chinese companies in patent quality, application scenarios, and global layout. It also points out that patent quantity does not equate to comprehensive technological leadership, emphasizing the challenges of manufacturing costs and quality, as well as the dual role of patents in industrial competition.

    Editorial context：The article presents a comprehensive analysis of China's growing dominance in humanoid robot patents, highlighting the shift from hardware to software innovation and the implications for global competition. It underscores the importance of patent strategy in shaping the future of the industry, while also acknowledging the challenges in translating patent strength into commercial success.

  2. arXiv Robotics — research abstracts63

    SkillWeaver: Agentic Exploration over Neural Interaction Skills for Scalable Robot Data Generation

    SkillWeaver is a framework that autonomously generates robot experience by exploring over Neural Interaction Skills (NIS), which are reusable, parameterized, closed-loop policies. It enables the agent to discover successful long-horizon behaviors through verifier-guided tree search, improving generalization across novel objects, tasks, and environments.

2026-09-23Wed
2026-09-22Tue
  1. Skild AI — Blog85

    Physical Self-Play

    Skild AI announces a breakthrough in physical AI through self-play, where a model like S1 learns to perform complex tasks such as soccer by competing against itself in simulation. The approach demonstrates the potential for robots to exceed human capabilities, drawing inspiration from historical AI advancements in game-playing.

    Editorial context：Skild AI introduces a new approach to physical AI through self-play, where a model like S1 learns complex tasks by competing against itself in simulation. This method shows promise for surpassing human capabilities in robotics, drawing parallels to historical successes in game-playing AI like AlphaGo and AlphaStar.

2026-09-10Thu
  1. Vikash Kumar ✈️IROS202627

    ✈️ I'm in LONDON🎡 Sept 14-17th Who should I meet? I'd love to connect with folks building in robotics / physical AI space. DMs open

    Quoted postLukas Ziegler@lukas_m_ziegler

    Another week, another robotics map! 🇬🇧 This time, we will take a closer look at the busy streets of London and see what robotics companies are located there. London has excellent engineers and researchers, especially from universities like Imperial College London and UCL, which are well known for robotics, AI, and engineering. Many robotics founders and early employees come directly from these universities. London is home to @GoogleDeepMind, one of the world’s leading AI labs. Its work on robot learning, control, and general AI has helped push forward how robots learn and adapt in the real world. The city also has one of Europe’s strongest investor ecosystems. London is a major global finance hub, so it’s easier to find venture capital, corporate investors, and early customers, especially for robotics companies working in areas like logistics, healthcare, and automation. It is very international and business-friendly. It’s easy to hire talent from around the world, set up a company, and sell globally. → @TheHumanoidAI builds general-purpose AI-driven humanoid robots capable of physical tasks across domains. → Automata Tech develops easy-to-deploy robotic automation hardware and software for SMBs to reduce manual labor. → @shadowrobot creates advanced dexterous robotic hands and manipulation systems for research and industrial automation. → Paddington Robotics designs small autonomous robots for retail and service environments to assist staff. → @KAIKAKU_AI builds robotics and AI enterprise solutions to optimize warehouse and logistics processes. → Automated Architecture (AUAR) develops spatial computing and robotic systems that blend physical and digital environments for construction and design. → @recycleye creates AI-powered robotics that identify, sort, and automate recycling and waste processing, and has raised ~$20M+ in funding. → @Neuracore_AI builds AI-based perception and planning software for autonomous robots, and has raised $10M+ in venture funding. → @apianhealth_ develops autonomous robotic systems for automated medication dispensing and hospital logistics. → @SlamcoreLtd offers high-performance SLAM navigation and vision software to help robots map and localize in complex environments, and has raised ~$6M+. → @MoleyRobotics builds fully automated robotic kitchen systems (“robotic chef”) and has raised tens of millions in funding (reports ~$30M+). → @dexoryHQ develops autonomous warehouse robots and AI software that continuously scan inventory and turn it into real-time operational insights, and has raised $80M Series B and a $165M Series C & growth round. → Extend Robotics builds tele-operation technology for robots, and helps with orchestrating the fleets of robots. It seems that if you wanted to explore London from the perspective of robotic startups, it would take a few days! What city should I do next? :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://ziegler.substack.com

2026-08-17Mon
  1. Skild AI — Blog85

    Introducing S1: In-Context Learning for Robotics

    Skild AI introduces S1, a robotic foundation model that leverages in-context learning to execute complex, long-horizon tasks without post-training. This marks a significant shift from traditional fine-tuning approaches, enabling rapid deployment and reducing data requirements. The model demonstrates strong performance on unseen tasks, including plant potting, pancake cooking, and kit assembly, and shows robustness to perturbations and common-sense reasoning.

    Editorial context：Skild AI introduces S1, a robotic foundation model that leverages in-context learning to execute complex, long-horizon tasks without post-training. This marks a significant shift from traditional fine-tuning approaches, enabling rapid deployment and reducing data requirements. The model demonstrates strong performance on unseen tasks, including plant potting, pancake cooking, and kit assembly, and

2026-04-14Tue
  1. Skild AI — Blog65

    Skild AI Acquires Zebra Technologies' Robotics Arm to Bring Omni-Bodied Intelligence to Warehouses

    Skild AI has acquired Zebra Technologies' robotics division to deploy its omni-bodied brain across warehouses, aiming to unlock massive productivity gains and enhance end-to-end fulfillment processes. This acquisition integrates Zebra's Symmetry Fulfillment platform with Skild's foundation model, enabling cross-embodiment generalization and intelligent decision-making in logistics environments.

    Editorial context：Skild AI's acquisition of Zebra Technologies' robotics division marks a significant step in deploying omni-bodied intelligence across warehouse environments. This move integrates advanced robotics platforms with Skild's foundation model, enabling cross-embodiment generalization without retraining. The collaboration aims to create a unified intelligence layer for logistics operations, enhancing end