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#Robot models and learning
Apr 20Mon
  1. PsiBot/灵初智能57

    Lingchu Intelligence Secures Investment from Guotou Xiandao and Jingxi Ruiling, Bolstering Embodied AI R&D and Commercialization

    Lingchu Intelligence announced the completion of a new funding round, with Guotou Xiandao and Jingxi Ruiling as investors; the funds will be used for core technology R&D and commercialization. On April 10, the company released its next-generation embodied AI models Psi-R2 and Psi-W0 and open-sourced a 1,000-hour full-modality human manipulation dataset, stating that Psi-R2 ranked No. 1 globally on the Molmospace benchmark; it will next advance the large-scale application of its dexterous manipulation solutions in logistics, industrial, and new retail scenarios.

  2. 普罗宇宙23

    Technology Empowers Intangible Cultural Heritage, Craftsmanship Passes Down Classics — ProUniverse Dabai Robot Appears at JD MALL

    The ProUniverse Dabai robot performed a dual-arm collaborative lacquer fan craft demonstration at the opening event of JD MALL (Wuhan Wangjiawan store), producing a customized lacquer fan within 1 minute. Its AI multimodal 3D vision and 2D industrial-grade positioning support sub-millimeter precision operation, and distributed learning enables rapid transfer of new scene skills. The event also introduced the "Craftsmanship Universe" plan, which, relying on the self-developed SDPAA parallelized data collection architecture and the InduThread-VLA large model, transforms traditional artisan skills into storable and replicable digital assets, with future expansion from crafts such as lacquer and sugar painting to precision assembly and high-end manufacturing fields.

Apr 17Fri
  1. Physical Intelligence Social59

    Physical Intelligence releases robot model π0.7, demonstrating emergent capabilities such as folding clothes

    Physical Intelligence announced that its latest model, π0.7, has several emergent capabilities: it can control a new robot to fold shirts without any shirt-folding data, can learn to use appliances through language guidance, and can complete multiple dexterous manipulation tasks within a single model.

Apr 16Thu
  1. Physical Intelligence77

    Physical Intelligence releases steerable general-purpose robot model π0.7, demonstrating compositional generalization

    Physical Intelligence has released the general-purpose robot model π0.7, which it says can perform a variety of dexterous manipulations with speed and robustness comparable to fine-tuned specialized models, and shows preliminary signs of compositional generalization on tasks not seen in its training data.

    Why it matters: The article uses two specific experiments, an air fryer and folding clothes with a UR5e, to show the actual performance of compositional generalization and cross-embodiment transfer, making it easier to judge the gap between general-purpose and specialized models.

Apr 15Wed
  1. Skild AI67

    Skild AI Acquires Zebra Technologies' Robotics Division to Advance Warehouse Deployments

    Skild AI announced the acquisition of Zebra Technologies' robotics division (formerly Fetch Robotics), and plans to deploy Skild Brain in warehouse settings to automate end-to-end fulfillment processes. Zebra's Symmetry Fulfillment scheduling platform has been validated in logistics environments, where it can coordinate tasks among robots and frontline workers; the company says the acquisition will accelerate its data flywheel and expand into more verticals and robot form factors.

    Why it matters: The acquirer itself explained the integration direction, so readers can judge how its platform and general-purpose models will be combined to enter warehouse settings.

Apr 13Mon
  1. 线性资本51

    UNICUS completes new round of several million USD led by Linear Capital

    Personalized manufacturing brand UNICUS (formerly Fangzai Photo Studio, Shenzhen Qianzhi Technology Co., Ltd.) announced the completion of a new round of financing of several million USD, led by Linear Capital, with JiuKun Venture Capital and Skyline Capital participating, along with personal participation from the founding team and founders of leading companies in the Maker sector; the funds will be used for building-block generation large model training, AI Agent development, and overseas market expansion.

Apr 11Sat
  1. 线性资本57

    Simple AI completes third funding round in six months, raising hundreds of millions of yuan, led by Linear Capital and Puhua Capital

    General-purpose embodied intelligent robotics company Simple AI announced the completion of its third funding round within six months, raising hundreds of millions of yuan, led by Linear Capital and Puhua Capital, with existing shareholders Junshan Capital, Shunwei Capital, and BV Baidu Ventures continuing to increase their investment; the funds will be focused on R&D of embodied robot brains and bodies.

Apr 10Fri
  1. PsiBot/灵初智能74

    Lingchu Intelligence Releases Psi-R2 and Psi-W0 Dual Models, Pretrained on 100,000 Hours of Human Data

    Lingchu Intelligence released two models, Psi-R2 and Psi-W0, which it calls the industry's first embodied intelligence models pretrained on human data at the 100,000-hour scale.

    Why it matters: The original article, written by the team itself, breaks down the dual-model architecture and data governance conclusions, offering a reusable path for human data utilization along with experimental evidence.

Apr 9Thu
  1. Zerith Robotics/零次方62

    Zero Power Robot completes over 100 million yuan funding round led by Runze Group

    Zero Power Robot announced the completion of a new funding round of over 100 million yuan, led by Runze Group, with Ningbo Dongli, Jieli Angel, and Pinghu Zexin participating. The company said its order count has exceeded 100 million yuan and revenue is in the tens of millions, with stable monthly mass production of 100 units, and validation completed in more than 20 landmark scenarios nationwide; the article also introduced its technological progress including the 4D augmented model ZERITH-SDA and the brain-inspired dual-stream asynchronous execution architecture ZERITH-V2.

    Why it matters: The company self-reports its funding details and order scale, and explains its scenario deployment path and data augmentation method, offering insight into its commercial and technological layout.

Apr 2Thu
  1. Tokyo Robotics · YouTube28

    Torobo humanoid's human-like walking and whole-body control

    Tokyo Robotics demonstrated human-like walking, whole-body teleoperation, and motion tracking on its custom-built Torobo humanoid. The control policies are trained through large-scale parallel reinforcement learning, and policies learned in physics simulation, once deployed to the real hardware, achieved dynamically stable whole-body motion. The company says it will continue improving motion stability in both software and hardware, and plans to introduce AI for autonomous task execution.

  2. Sanctuary AI55

    Sanctuary AI demonstrates zero-shot in-hand manipulation with hydraulic dexterous hand

    Sanctuary AI demonstrated zero-shot in-hand manipulation in the real world with its proprietary hydraulic hand: a policy trained entirely in simulation continuously reoriented a lettered cube to a target pose ten times in a row without dropping it. The operation relied solely on the fingertips with no palm support, requiring simultaneous stable grasping and approach to the pose target. The original text states that the hand has a relatively high number of active degrees of freedom enabling finger abduction, and that hydraulic actuation and compact hydraulic valves offer a path toward industrial-grade humanoid dexterous manipulation.

Mar 24Tue
  1. Agile Robots/思灵机器人66

    Agile Robots and Google DeepMind announce strategic partnership to integrate Gemini Robotics models into industrial robots

    Agile Robots SE and Google DeepMind announced a strategic research partnership to combine the Gemini Robotics foundation models with Agile Robots' industrial robot platform and develop adaptable, reasoning-capable robots. The partnership initially focuses on industrial manufacturing scenarios where reliability and scale are critical, forming a data flywheel through deployment, data collection, model training, and iteration; Agile Robots says it has installed more than 20,000 robot solutions worldwide.

    Why it matters: The partnership focuses on industrial deployment and data feedback for training, giving readers insight into the path to real-world adoption when foundation models are connected with robot manufacturers.

  2. Berkeley AUTOLAB68

    CaP-X: Benchmarking and Improvement Framework for Robot Manipulation Coding Agents Released

    Teams including Berkeley AUTOLAB propose CaP-X, an open framework for systematically studying Code-as-Policy agents in robot manipulation, centered on the interactive environment CaP-Gym and CaP-Bench, which evaluates 12 frontier models.

    Why it matters: The original text presents a systematic evaluation framework for Code-as-Policy agents and the finding that test-time scaling can mitigate abstraction dependence, offering reference value for research on coding-agent manipulation control.

  3. Dobot/越疆机器人64

    Dobot begins mass production delivery of third batch of full-size industrial humanoid robot ATOM

    Dobot Robotics has officially begun mass production delivery of the third batch of its full-size industrial humanoid robot ATOM, deploying it to frontline industrial applications worldwide. ATOM runs on the self-developed DOBOT-VLA model, which combines vision, language, and action for autonomous decision-making; its joint modules have undergone nearly 10,000 cycle tests, and it supports coordinated scheduling with wheeled and multi-legged robots on a unified platform.

    Why it matters: The source, provided by the party involved, describes progress on the third batch's mass production delivery and its deployment in real scenarios, giving readers a sense of the pace of its industrialization.

Mar 20Fri
Mar 19Thu
  1. Physical Intelligence60

    Physical Intelligence proposes RLT: efficient online reinforcement learning for fine manipulation

    Physical Intelligence proposes a new method called RL tokens (RLT), in which a vision-language-action model (VLA) outputs a special token to interface with a lightweight online reinforcement learning policy, improving the speed and success rate of fine manipulation phases with only a small amount of real data.

    Why it matters: The original clearly explains the interface design between the VLA and lightweight online reinforcement learning and provides speedup data on four tasks, so readers can assess whether this type of method applies to their own fine manipulation tasks.

  2. Skild AI75

    Skild AI partners with ABB Robotics, Universal Robots, and NVIDIA to deploy Skild Brain

    Skild AI announced partnerships with ABB Robotics and Teradyne-owned Universal Robots and MiR to integrate its general-purpose model Skild Brain into industrial robots, eliminating task-by-task programming and using post-training fine-tuning with a small amount of task data.

    Why it matters: Skild AI describes its own partnership deployment approach with ABB, UR, and NVIDIA and assembly task details, letting readers understand how its general-purpose model enters production lines.

Mar 18Wed
Mar 16Mon
  1. RoboForce72

    RoboForce closes $52 million funding round, bringing total funding to $67 million

    RoboForce announced an oversubscribed $52 million funding round, bringing its cumulative funding to $67 million. The round was led by YZi Labs, with participation from Jerry Yang. The new capital will be used to accelerate next-generation robot foundation models, scale general-purpose Physical AI robots, and advance manufacturing readiness for commercial deployment.

    Why it matters: The funding announcement also explains the use of funds and the technology stack partnership with NVIDIA, which readers can use to understand the company's path to deployment in its next stage.

Mar 9Mon
  1. Figure AI57

    Figure AI demonstrates Helix 02 tidying a living room end-to-end with full-body control

    Figure AI released a new Helix 02 demo in which a single neural network system completes an end-to-end living room tidying task with full-body control, continuously manipulating objects, tools, and containers while walking. In the demo, Helix 02 uses no new algorithms or special engineering; with only added data, it learned to wipe and clean with a spray bottle and towel, collect blocks into a box carried with both hands, toss a pillow back onto the sofa, turn around while holding a remote and then press a button to turn off the TV, and sidestep through the narrow gap between the coffee table and the sofa.

Mar 4Wed
Mar 3Tue
  1. Physical Intelligence78

    Physical Intelligence proposes MEM, enabling π0.6's VLA to remember long-horizon tasks of up to 15 minutes

    Physical Intelligence proposed a method called Multi-scale Embodied Memory (MEM) that provides vision-language-action models (VLAs) with both short-term and long-term memory, enabling the model to complete tasks lasting up to 15 minutes, such as cleaning an entire kitchen or making a grilled cheese sandwich from scratch.

    Why it matters: The article identifies memory mechanisms as the bottleneck for long-horizon tasks and presents a dual-scale design that stores video for the short term and language for the long term, an approach that can be transferred to other work.

Feb 25Wed
  1. 普华资本62

    Boundless Dynamics completes over 200 million yuan Angel+ round, third round nearing completion

    General-purpose embodied intelligence robotics company Boundless Dynamics announced the completion of an over 200 million yuan Angel+ funding round, jointly invested by Puhua Capital, Shenzhen Capital Group, Innovation Works, Junshan Capital, and Yari Capital, with existing shareholders Sequoia China, Linear Capital, and Hillhouse Venture Capital increasing their investments. The third round of financing is nearing completion, and cumulative funding will reach 800 million yuan.

    Why it matters: The original article disclosed the funding scale, technology roadmap, and deployment stage, allowing readers to judge this embodied intelligence company's commercialization progress and R&D direction.

  2. Physical Intelligence Social35

    Physical Intelligence builds a physical intelligence layer

    General-purpose AI models underpin some of the most exciting applications we now can't live without. We envision that an analogous "physical intelligence layer," built with models like π0.6, will similarly drive a new wave of applications in the physical world. We have recently begun working with a small number of companies whose robots are already performing practically useful tasks in the real world. https://www.pi.website/blog/partner/?v=1

Feb 24Tue
  1. Physical Intelligence74

    Physical Intelligence proposes a physical intelligence layer and publishes π 0.6 deployment data from Weave and Ultra

    In a blog post, Physical Intelligence argues that robots need a reusable physical intelligence layer, and it published real-world deployment data from partners Weave and Ultra using π 0.6.

    Why it matters: The two deployers provided comparative data on autonomy rates, missed grasps, and throughput, making it easier to judge the practical gains that foundation model iterations bring in real-world settings.

Feb 13Fri
  1. 普华资本58

    Puhua Capital Leads RoboScience's Pre-A Funding Round of Several Hundred Million Yuan

    RoboScience has announced the completion of a Pre-A funding round of several hundred million yuan, led by Puhua Capital, with participation from Fortune Capital, Changshi Capital, Hong Kong Huike Innovation, and Tianqi Capital, while existing shareholders including China Merchants Venture and Zero One Venture continued to increase their investment. The funds will mainly be used to deepen its core VLOA large model technology.

  2. Samsung NEXT · X54

    Samsung Next and NVIDIA's NVentures invest in Odyssey ML to accelerate general world simulator research

    Odyssey ML announced it has received investment from NVIDIA's venture arm NVentures and Samsung Next to accelerate its research on general world simulators. Samsung Next reposted the news, saying it is excited to be part of Odyssey ML's journey, and included a link to the original tweet: https://x.com/odysseyml/status/2021997012938580367.

Feb 11Wed
Feb 5Thu
Feb 2Mon
  1. 线性资本62

    Interview with Tashi Intelligence's Chen Yilun: No VLA, no simulation—an embodied AI company's contrarian bets

    Linear Capital reposted LatePost's interview with Tashi Intelligence founder Chen Yilun, who laid out a technical path that differs from the mainstream: he rejects the VLA architecture that grows an action head on top of a VLM, and instead develops its own AWE (AI World Engine), aiming to represent physical quantities such as time, space, and force; it has also abandoned teleoperation data collection in favor of a self-developed collection device combining a wearable glove and a first-person-view camera.

    Why it matters: The interview systematically presents technical judgments and a data approach that differ from the mainstream VLA path, giving readers a basis for understanding the specific grounds of the divergence in embodied AI approaches.

Jan 27Tue
  1. Figure AI82

    Figure AI Releases Helix 02: A Full-Body Autonomous Control Model for Humanoid Robots

    Figure AI has released Helix 02, extending control from the upper body to the entire humanoid robot and enabling continuous, integrated autonomous tasks combining locomotion and manipulation. It adds a new System 0 whole-body controller, trained on over 1,000 hours of human motion data and simulation-based reinforcement learning, which outputs joint commands at 1 kHz via a 10M-parameter network, replacing roughly 109,504 lines of hand-written C++ code; System 1 connects head-mounted vision, palm cameras, fingertip touch sensing, and proprioception, outputting whole-body joint targets at 200 Hz.

    Why it matters: The original text provides details on Helix 02's architectural layers, training data, and demonstration tasks, allowing readers to understand the specific implementation approach for full-body autonomous control.

Jan 19Mon
  1. Sharpa49

    Sharpa showcases fine-manipulation autonomous live demos at CES 2026 with CraftNet VTLA model and North robot

    At CES 2026, Sharpa debuted North, a full-body robot driven by the CraftNet end-to-end hierarchical VTLA (vision-tactile-language-action) model, running live autonomous demos for eight hours a day over four consecutive days: taking photos, table tennis rallies, windmill assembly, and card dealing, all without scripts.

Jan 14Wed
  1. Skild AI82

    Skild AI announces completion of $1.4 billion Series C funding, valuation exceeds $14 billion

    Skild AI announced the completion of a $1.4 billion Series C funding round led by SoftBank, with participation from NVentures (NVIDIA), Macquarie Capital, Jeff Bezos, and others, at a valuation exceeding $14 billion.

    Why it matters: The official disclosure of funding size, valuation, and revenue data gives readers insight into the commercialization progress of this general-purpose robotics model company.

Jan 12Mon
  1. Skild AI48

    Skild AI: Enabling robots to learn new skills by watching human videos

    Skild AI proposes overcoming the robot data bottleneck by watching human videos, arguing that teleoperation is limited by environment diversity and real-time collection scale and cannot reach the data volume required by foundation models. Its approach is cross-embodiment learning: with less than 1 hour of robot data for fine-tuning, the model can learn new skills from video demonstrations alone, mapping human "grasping" onto the execution actions of different robot embodiments.

  2. LimX Dynamics58

    LimX Dynamics releases LimX COSA embodied agent system, demonstrated on Oli humanoid robot with mobile manipulation

    LimX Dynamics has officially released LimX COSA (Cognitive OS of Agents), an embodied agentic OS positioned as native to the physical world. The system uses a bottom-up three-layer structure: the bottom layer is a whole-body motion control foundation model, the middle layer is a high-level skill layer integrating large and small models, and the top layer is autonomous cognition and decision-making, involving interaction, memory, and thinking.

Jan 7Wed
  1. Mentee Robotics · YouTube48

    MenteeBot learns to replace a battery by observing human demonstration (no teleoperation): Observe → Train → Perform (Real2Sim2Real)

    Mentee Robotics demonstrated a MenteeBot learning to replace the battery in another MenteeBot without teleoperation, solely by observing a human demonstration. The learning process has three Real2Sim2Real stages: the robot records a human operation video from its own viewpoint, a foundation model reconstructs the task in simulation and then trains at scale using self-play and reinforcement learning, and finally its proprietary Sim2Real technology transfers the skill to autonomous execution on the real robot.

  2. Sharpa53

    Sharpa releases CraftNet, a hierarchical VTLA model for fine manipulation

    Sharpa has released CraftNet, an end-to-end hierarchical VTLA (vision-tactile-language-action) model aimed at last-millimeter contact control in fine manipulation. The model consists of three parts: System 2 (about 1 Hz, an open-source VLM responsible for reasoning and task decomposition), System 1 (about 10 Hz, responsible for motion planning and coarse actions), and System 0 (about 100 Hz, performing real-time fine-tuning of hand movements based on tactile feedback), with the three cooperating asynchronously.

Dec 23Tue
Dec 22Mon
  1. 线性资本58

    Tashi Intelligence Makes Full-Stack Tech Debut with Autonomous Embroidery Robot and Wiring Harness Manufacturing Solution

    On December 19, Tashi Intelligence, founded just 10 months ago, held a livestreamed full-stack technology debut. The world's first robot to achieve autonomous embroidery was officially unveiled, completing thread pulling, needle lowering, and piercing motions with sub-millimeter precision to embroider the brand logo, while also demonstrating the autonomous implementation of flexible fine manipulation in wiring harness assembly.