Today's Highlights
· The US FCC has added foreign-made humanoid and quadruped robots to its "Covered List" — new models can no longer obtain the equipment certification required to enter the US market
· Momenta secured an L4 testing license covering all of Germany, the first for a Chinese company, eliminating the need for city-by-city applications
· Seven Chinese government departments set hard 2028 targets via an open-competition program: on-device inference chips at 200TOPS, power consumption under 15W
· HiFi-UMI post-trains using only data collected without a robot, matching teleoperation-level success rates on real hardware
· Hyundai plans to build a US factory, targeting humanoid robot capacity of up to 30,000 units by 2028
I. Research Progress
HiFi-UMI: Training deployable real-robot policies without any robot-collected data · manipulation
Topping today's HF trending score at 96, the highest of the batch. Handheld UMI collection is cheap and scalable, but the field has long treated it only as pretraining data — post-training still needed a small slice of real-robot teleoperation data as an "anchor." This paper flips the question: if UMI data fidelity is pushed high enough, can that anchor be eliminated entirely? At the system level, head-mounted offline binocular-inertial SLAM, native relative pose, microsecond-level GPIO synchronization, and two roughly 200° wide-angle cameras per hand achieve 3mm end-effector precision in the workspace without external motion capture. The result: policies post-trained on pure HiFi-UMI data deployed directly to real robots, across three backbones (StarVLA-QwenPI, OpenPI-pi_0.5, LingBot-VA), differ from teleoperation baselines collected within the evaluation scenes by only −2.5/+3.1/−0.6 percentage points in success rate, reaching up to 85% on precision insertion tasks. The team also open-sourced HiFi-UMI-2K, 2,000 hours of microsecond-synchronized, ultra-wide-field-of-view demonstration data.
Simple AI team · arXiv 2607.25895 source
πR²: Making flow policies react mid-execution, replanning 4x faster · manipulation
Action-chunking flow policies run open-loop — once a chunk of actions is issued, they're deaf to new observations arriving mid-execution, which is the primary failure mode in dynamic scenes. πR²'s approach splits conditioning into two channels: proprioception runs on a "fast channel" refreshed every tick, while vision-language features run on a "slow channel" updated asynchronously, paired with a latency-adaptive flow scheduler that treats in-flight actions as inpainting conditions and performs only one denoising step per call. Built on GR00T-N1.7, on a real xArm6+XHand platform, closed-loop replanning is roughly 4x faster than the base policy (about 25Hz on an A5000, incorporating a new frame's observation every 40ms), with success rate gains of up to 23% in simulation and 30% on real hardware.
Sungjae Park, Shubham Tulsiani (CMU) · arXiv 2607.26055 source
Transformer Transformer: Generating the robot's own embodiment · manipulation
Manipulation performance is often limited not by the policy but by the embodiment itself. This paper frames embodiment design as a diffusion generation problem: a diffusion transformer trained on unified RoboTokens (a unified tokenization of embodiment, state, and action), with the same architecture applicable across design spaces including wheeled bimanual arms, quadrupeds, and humanoids. The key design choice is treating it as a dynamics model rather than fitting to a single reward — reward-agnostic state-action prediction is converted into reward-specific value prediction, which guides embodiment diffusion. The team actually built the optimized ALOHA design, reducing tracking error by over 70% compared to the original design. The authors are from Stanford's Shuran Song and C. Karen Liu labs.
Huy Ha et al. (Stanford) · arXiv 2607.25798 source
CoTinyVLA: A 0.9B model beats 7B models on robustness benchmarks · vla
The top of the LIBERO-Plus leaderboard has long been dominated by 3B–7B backbones, a memory footprint that's a hard blocker for embedded robots. CoTinyVLA uses a Qwen3.5-0.8B backbone with 0.9B parameters, achieving robustness through structured supervision rather than scaling up: 16-frame dual-view history input per step, a hierarchical chain-of-thought distilled from a 35B teacher (episode-level "Plan" plus chunk-level "Think"), and rewriting augmentation that expands 40 base instructions into 800. On LIBERO-Plus, which spans seven perturbation dimensions and 10,030 tasks, it scores 90.8%/87.3%/86.6%/80.7% on Spatial/Object/Goal/Long respectively, outperforming the strongest 7B baseline across the board; peak closed-loop inference memory is only 2.25 GiB.
Minhyeok Lee et al. · arXiv 2607.25487 source
When does data from old hardware become useful again? There's a "phase-transition threshold" · manipulation
Robot hardware is updated year after year, but demonstration data is tied to specific sensor and actuator configurations. The team ran controlled experiments across two generations of wheeled humanoids with the same morphology but changed cameras and grippers, observing a grokking-like phase transition: below a minimum capability threshold on the new configuration, old data is entirely useless (10.0% → 10.0%); past the threshold, co-training gains spike sharply (flower insertion 23.3% → 86.7%); near saturation, gains decay rapidly (pen insertion 85.0% → 93.3%). This three-stage pattern yields a practical rule: first determine which phase the current capability falls into, then decide whether to collect more data on new hardware or reuse old demonstrations.
Tao Wang et al. · arXiv 2607.25593 source
Pictura: The first large-scale driving policy trained via self-play directly from perspective images · autonomy
Simulated self-play can produce robust driving policies at scale, but prior work has all relied on privileged vectorized observations that assume perception is already solved, then distilled to a camera-based student that can only imitate decisions it has no visual basis for understanding. Pictura is a GPU-accelerated multi-agent driving simulator that renders each agent's first-person view at every step, reaching 500,000 agent-steps/s on a single H100. Alberti, trained on it with vanilla PPO, ran for 50 billion agent steps — about 35 million kilometers — approaching the performance of privileged vectorized opponents and outperforming them when zero-shot transferred to Waymo Open Motion Dataset layouts. From Valeo AI.
Yuan Yin et al. (Valeo AI) · arXiv 2607.26005 source
INTACT: World models no longer need test-time search to infer actions · world-model
Forward latent-space world models can predict how an action changes the world, but inferring "what action achieves this change" has required expensive test-time search. INTACT uses an end-to-end JEPA to turn action-labeled, reward-free trajectories into a directly deployable intent-action interface, where the mean of the conditional distribution is itself a search-free policy. Across four official LeWM tasks, the single-pass trained zero-search model reaches 85.78%/100.00%/97.67%/97.89%; an optional local CEM needs only 384 candidate sequences (down from 9,000) to achieve 96.86% macro success rate, a 23.44x reduction in sample count, with direct inference taking 2.9–5.5ms.
Junhan Sun, Hao Zhao, Guofeng Zhang · arXiv 2607.26056 source
DC-WAM: Stop wasting compute rendering textures in world-action model video branches · world-model
World-action models supervise policies by predicting future frames, but photorealistic reconstruction devotes substantial compute to textures, lighting, and background — things weakly related to action selection. DC-WAM reallocates the RGB branch's supervision and compute toward "interaction-induced visual dynamics": the supervision layer uses temporal-difference flow matching with trajectory-guided weighting, focusing on changes in the gripper, manipulated object, and contact regions; at inference, DynaRoute predicts token-level dynamics relevance and converts it into attention bias. Experiments in simulation and on real hardware show consistent policy performance gains, with the largest improvements under out-of-distribution perturbations in lighting, object appearance, and background texture.
Haoyuan Ji et al. · arXiv 2607.25918 source
Using SAM3D as a "3D teacher" to distill π₀, hitting 99.1% on LIBERO · vla
Most VLAs are built on 2D vision-language backbones, lacking fine-grained 3D understanding under occlusion, pose changes, and scale changes. This framework performs object-centric 3D representation alignment on top of π₀: during training, a recognition model localizes task-relevant objects and generates masks, then a frozen SAM3D extracts dense object-level 3D representations that are aligned with π₀'s intermediate visual features — at inference, no depth, point cloud, mask, or extra 3D module is needed, and the original RGB-language-to-action pipeline is unchanged. It reaches 99.1% on LIBERO, an average length of 4.11 on CALVIN, with gains particularly pronounced on real-robot long-horizon manipulation tasks that require switching target objects across multiple subtasks.
Zonghe Liu et al. (University of Hong Kong) · arXiv 2607.25912 source
Other papers today: SONG builds a photorealistic social navigation simulation platform using 3D Gaussian splatting, with pedestrian trajectories driven by LLM semantics (arXiv 2607.25219 source); a causality-aware infer-diagnose-correct framework dynamically adjusts a VLA's reliance weight on vision at test time, training-free and pluggable into multiple backbones (arXiv 2607.25516 source); implicit contact differentiation distills batch full-horizon iLQR into a guided short-horizon residual MPC policy (arXiv 2607.24959 source); TriManPolicy lets a single operator demonstrate tri-arm coordination, addressing the mismatch between teleoperation channel count and robot degrees of freedom (arXiv 2607.25731 source); a systematic survey of "environmental constraint exploitation" — actively using contact with the environment to reduce dimensionality and simplify motion planning (arXiv 2607.25053 source); FIRMGrasp uses CVaR risk measures to characterize grasp force-closure margins under friction coefficient uncertainty (arXiv 2607.25049 source); Calibrated Partial Resets scales reset strength back toward initialization by neuron utility, mitigating policy collapse in continual reinforcement learning (arXiv 2607.24996 source); using a VLM to extract landmarks from street-view panoramas and match them against OpenStreetMap semantic priors for city-scale cross-view localization (arXiv 2607.25215 source); P3 addresses the mismatch between VAE latent variables and PPO's probability-ratio estimation, switching to a method-of-moments approach for distribution-aware optimization (arXiv 2607.25541 source); S2A2 incorporates acoustic spatial information into imitation learning, using auditory cues to determine manipulation targets (arXiv 2607.26047 source); BAIT explicitly shapes a human's beliefs during repeated human-robot interaction, matching an unpredictability baseline on performance but with significantly higher user trust (arXiv 2607.25327 source); Temporal-Distance JEPA adds multi-step goal-progress ranking to latent-space world models for predictive control (arXiv 2607.25337 source); VisualPatchWorld represents world dynamics as code, first selecting a qualitative form via a short probe then fitting parameters (arXiv 2607.25236 source); a four-wheel independent-drive vehicle achieves steerless drift control via differential torque (arXiv 2607.24863 source); SGTP combines game-theoretic reasoning with GPU-accelerated sampling to support real-time racing planning for up to 10 vehicles (arXiv 2607.25388 source); IMPRINT enriches text queries with web images, improving grounding quality for long-tail object navigation (arXiv 2607.25106 source); a modular robotic catheter system targets fenestrated/branched endovascular repair of abdominal aortic aneurysms (arXiv 2607.25807 source); Room-Mediated Co-occurrence mediates object relationships through a compact room vocabulary for training-free zero-shot semantic navigation (arXiv 2607.25448 source); HOME matches 1D extrema in Hough space instead of point features, for strongly linear-structured, textureless scenes (arXiv 2607.25389 source); Tripody is an overconstrained parallel robot for high-elevation construction work (arXiv 2607.25781 source); a robotic fish performs first-person station-keeping in unknown turbulent background flows (arXiv 2607.24860 source).
II. Funding & Deals
Yaole Technology | Pre-A+ round | Amount undisclosed · hardware
Led by Dinghe Gaoda, with listed companies Changshu Automotive Trim and Zulong Entertainment participating, and Yundao Capital serving as long-term exclusive financial advisor; the company had previously secured an exclusive angel round from Xiaomi, plus a Pre-A round from Yinfeng Fund (backed by Ecovacs), Silicon Harbor Capital, and Chuhui Capital. Yaole started with automotive-grade fabric sensors, but positions itself beyond selling tactile sensors to robotics companies — it aims to turn the pressure, posture, and feedback of human contact with the physical world into a data asset that can feed directly into world-model training pipelines, via carriers like gloves, seats, and mattresses that people naturally touch. Founder Lyu Liyin previously served as chief architecture engineer in Harman's global innovation division, leading a multimodal sensor fusion platform and serving over a dozen OEMs including Maserati and Porsche. ⚠️ Single-party account ("tenfold order growth" is company-disclosed) Source: 36Kr source
UNIT AI (US) | New round | $12 million · industrial
Co-led by Prologis Ventures, Dynamo Ventures, and Ground Up Ventures, with participation from eGateway Capital and others. Founder Guy Glass previously co-founded warehouse automation company Caja Robotics in 2014, which was acquired by France's Fives Group in 2025; this time he's targeting small and mid-sized retailers and third-party logistics providers who can't afford that tier of automation: the modular system claims to fit into a 1,000-square-foot space without tearing out existing shelving or flooring, with a 12-month payback period. The company is 22 months old, with customers including Barrett, ShipCalm, DaVinci, and Carter. ⚠️ Vendor account (installation footprint and payback period are self-reported by the company) Source: Tech Funding News source
Industry data: China's embodied AI "20-billion-yuan club" has grown to 8 companies · embodied
As of June 2026, eight Chinese embodied AI companies have reached valuations of 20 billion yuan or more — Unitree, Agibot, Galbot, AgiBot's rival Star Age (Xinghai Tu), Qianxun Intelligence, X Square Robot, Zhiping Fang, and Lingxin Qiaoshou — but their valuation logic splits into three camps: Unitree and Agibot have proven a hardware-sales flywheel through actual shipments and cost control; five "brain-focused" companies command a valuation premium on model and data moats against future potential; Lingxin Qiaoshou makes only dexterous hands, not full robot bodies, and passed 20 billion yuan in valuation after an April Series B+ round. Financially, Unitree is a rare case of an already-profitable player, with 2025 revenue of 1.699 billion yuan and non-GAAP net profit of 590 million yuan; it has completed STAR Market IPO registration at an issuance valuation of roughly 42 billion yuan, implying a price-to-sales ratio of about 24.7x — compared with a maximum P/S ratio of about 10x for Fanuc, the world's largest industrial robot manufacturer by installed base, meaning the difference reflects the sector's valuation premium; Agibot's 2025 revenue exceeded 1.05 billion yuan, with profitability undisclosed. Source: Sina Finance source
Standard Robots | Third IPO filing with Hong Kong Stock Exchange | Revenue up 139% in first four months, loss of RMB 61.84 million · industrial
Following its third IPO application to the Hong Kong Stock Exchange on July 27, the prospectus materials reveal the underlying figures: revenue grew 139% year-on-year in the first four months of 2026, alongside a loss of RMB 61.84 million in the same period. The company's core business is integrated industrial intelligent robot solutions. Source: Forbes China source
Zhigu Tianchu (Shenzhen) | Strategic funding round | Nearly RMB 100 million · adjacent
Following its disclosure of a nearly RMB 100 million strategic funding round on July 27, the investors are now confirmed as China Merchants Capital Investment leading, with Leap Capital participating, and existing shareholders Yunqi Partners and Qifu Capital adding over-allotment investments; the company simultaneously announced it will rename itself "Qianyi Intelligence." This maker of smart cooking robots has, unusually for the sector, achieved profitability — per its own disclosure, 2025 revenue exceeded 100 million yuan with the company turning a profit, and it once secured a single order worth 150 million yuan. ⚠️ Single-party account (revenue and profitability status are self-disclosed by the company) Source: Robot Lecture Hall source
III. Commercialization & Deployment
Momenta secures L4 urban road testing license covering all of Germany, a first for a Chinese company · autonomy
Germany's Federal Motor Transport Authority (KBA) has issued a license to Momenta permitting L4 autonomous driving tests on urban roads nationwide across Germany; the company says this is the first time a Chinese company has obtained an L4 testing authorization covering the entirety of Germany's urban road network. The license's practical value lies in two points: first, no longer needing to file city-by-city applications, and second, its safety validation standard aligns directly with the EU regulatory framework. The KBA is regarded as one of the world's strictest traffic safety regulators, and its approval itself serves as an endorsement of the technology's maturity. Momenta has partnered with Uber, planning to make Munich its first Robotaxi service city in Europe. Uber has simultaneously been deepening its financial ties: its wholly owned subsidiary SMB Holding was allotted 397,600 shares in Momenta's Hong Kong IPO this month, and combined with 791,000 preferred shares held from the September 2025 Series C round, Uber's total stake of roughly 1.19 million shares is valued at about HK$351 million (roughly $45 million) at the HK$295.6 issuance price. Momenta listed on the Hong Kong Stock Exchange on July 8, raising about HK$6.8 billion, roughly a fifth of which is earmarked for advancing Robotaxi commercialization. Source: CnEVPost source
Canada's largest freight company TFI plans to deploy driverless heavy trucks in the US next year · autonomy
TFI International CFO David Saperstein said on the company's earnings call that it is partnering with a major US autonomous trucking technology provider, planning to begin testing within a few months and deploy driverless heavy trucks in the US next year, running fixed long-haul routes between terminals and distribution centers. His stated rationale is availability rather than pure cost-cutting: "it can run around the clock, no hours-of-service limits," with smoother acceleration and deceleration bringing fuel savings, while also avoiding driver turnover and no-shows. TFI's second-quarter net profit rose 39% year-on-year to $136.2 million, with revenue up 12% to $2.29 billion. Source: BNN Bloomberg source
Hai Robotics equips LPP's Romanian fulfillment center with 948 robots · industrial
The logistics subsidiary of Central and Eastern European fashion retail group LPP has deployed Hai Robotics' HaiPick System 3 at its new fulfillment center near Bucharest, a goods-to-person system made up of 278 autonomous case-handling robots (ACRs) and 670 AMRs, providing over 625,000 automated storage locations capable of holding 7.5 million apparel and accessory items — the entire system was deployed in under six months without interrupting existing operations. The system can process over 9,400 totes per hour, with the center designed to handle over 80,000 orders daily, automated sorting capacity of up to 14,000 parcels per hour, serving e-commerce operations across Romania, Bulgaria, Serbia, Greece, and Hungary. Source: Robotics & Automation News source
Shimizu Corporation puts an autonomous humanoid robot on the construction site of Japan's tallest building · humanoid
Japanese construction giant Shimizu Corporation is testing an AI humanoid robot at its Tokiwabashi project (set to become Japan's tallest building upon completion), where the robot, equipped with cameras and sensors, patrols along preset routes at 1 meter/second to conduct inspections, with Sony and other companies as technology partners. Shinichi Sakamoto, deputy manager of the company's construction technology department, drew a clear line against the company's past site robots: "All the previous ones needed human assistance to move, and the work quality required extensive rework and adjustment — many said humans were faster. Now none of those are left." The company says the new robot will also learn spray painting from workers. ⚠️ Vendor account (multi-task capability and spray-painting learning are company statements; still in testing) Source: Global Construction Review source
China's first autonomous airport passenger shuttle project launches in Urumqi · autonomy
The first batch of Yutong autonomous passenger shuttle buses has officially entered service at Urumqi Diwopu International Airport, marking China's first autonomous airport passenger shuttle project. The airport's enclosed perimeter with fixed routes is a scenario with relatively lower L4 commercialization difficulty, but with very high requirements for punctuality and safety redundancy. Source: Xinhua Net source
US Robotaxi city list keeps growing: ID. Buzz hits LA streets, Lucid×Uber's next stop is Houston · autonomy
Volkswagen's ID. Buzz Robotaxi has begun operating on the streets of Los Angeles, while the Lucid-Uber Robotaxi program has named Houston as its next city, with a key milestone expected in August. This wave of expansion contrasts with Momenta's German license covered above in this section: growth in the US comes from fleets covering more cities, while in Europe the bottleneck is regulatory authorization. Source: InsideEVs source
IV. Industry Developments
FCC adds foreign-made advanced robots to "Covered List," cutting off equipment certification for new models entering the US · humanoid
The US Federal Communications Commission added foreign-manufactured advanced robotics equipment (humanoid, quadruped, and connected machines whose movement or manipulation depends on software, firmware, or AI, including wheeled and tracked types) along with connected power inverters to its "Covered List" on the afternoon of July 28 local time, meaning new models can no longer obtain the equipment certification required for import, marketing, and sale in the US. The rule took effect immediately and applies only to new models not yet authorized; models already authorized and on sale in the US are unaffected. FCC Chairman Brendan Carr called the move a step to "protect the security of America's critical supply chains," and a government official told CNBC the concern centers on data theft, remote control, and the threat of cyberattacks against US AI infrastructure, while also aiming to encourage manufacturing to return to the US. China's embassy in Washington responded that it opposes politicizing trade issues and opposes sanctions based on unfounded pretexts. Notably, the rule text uses "foreign-manufactured" rather than naming China — but the company clearly implicated is unambiguous: Unitree (Chinese humanoid/quadruped robot maker) is squarely in the crosshairs. According to a teardown report from SemiAnalysis, Unitree's G1 has a bill-of-materials cost under $9,000, sells for $27,300 including tax, with a gross margin over 60%, with some bulk deals reportedly closing below $20,000; meanwhile, over 70% of the humanoid robots Unitree exported in 2025 went to universities for research, with under 10% entering actual industrial applications. The ban can block finished-product imports, but it can't change the fact that the global supply chain — from motors and reducers to sensors — remains heavily dependent on Chinese suppliers, a dependency that even most US robotics startups themselves rely on. Source: CNBC source
Seven Chinese government departments set hard 2028 targets for embodied AI via open-competition program: chips at 200TOPS, datasets exceeding 10 million hours · embodied
China's Ministry of Industry and Information Technology, Ministry of Emergency Management, People's Bank of China, National Financial Regulatory Administration, China Securities Regulatory Commission, Chinese Academy of Sciences, National Cultural Heritage Administration, and other seven departments jointly issued a 2026 notice on open-competition innovation tasks in the industrial and information technology sector, organizing 24 topics across 6 directions including future industries, equipment manufacturing, and information technology. Under the "humanoid robots and embodied AI technology" topic, 18 tackling tasks are set out with verifiable quantitative targets. Embodied multimodal large models must integrate at least four modalities by 2028, achieve 96%+ multi-scenario perception and recognition accuracy, keep on-device inference latency within 70ms, and support 20+ mainstream robot models; on-device inference chips must deliver at least 200TOPS of half-precision compute, power consumption under 15W, latency under 40ms, and mass-production yield above 95%; high-quality datasets must total more than 10 million hours, with total data volume no less than 5PB, covering 30+ typical scenario categories; industrial-scenario humanoid robots must achieve positioning accuracy within ±0.3mm and dexterous manipulation success rates above 98%. The guidelines also bring upstream force-torque sensors, modular joints, dexterous hands, and servo drives, midstream on-device world models, VLAs, and virtual-physical integrated simulation platforms, and downstream deployment across four categories of full-robot scenarios all into the application scope — compared with the previously scattered local subsidies, this is the first time a national-level program has laid out the two-year technology roadmap with hard targets. Source: iitime source (WeChat, CN)
US Congress moves to legislate: AVs blocking ambulances and fire trucks would give local governments "geofencing" authority · autonomy
California Democratic Congressman Kevin Mullin will introduce the "AV Emergency Response Coordination Act" this week, requiring federal regulators to establish minimum national safety standards for autonomous vehicle operators. The trigger is a string of incidents: autonomous vehicles blocking ambulances and fire trucks, driving into active crime scenes, and failing to recognize safety signals like traffic cones and flares. The bill requires AV companies to provide clear emergency protocols to first responders, establish a 24-hour hotline for direct contact by public officials, and have NHTSA set minimum emergency response standards. For operators, the more difficult provision may be another one — allowing public officials to establish geofences around AVs during emergencies or ongoing safety hazards, effectively handing part of a fleet's operating boundaries to municipal authorities. San Francisco Mayor Daniel Lurie and Fire Chief Dean Crispen attended the announcement, with Crispen noting that Robotaxis have picked up and dropped off passengers directly in front of fire stations and ambulance dispatch points, obstructing emergency access. Source: TechCrunch source
Hyundai to build a US humanoid robot factory, targeting capacity of up to 30,000 units by 2028 · humanoid
Following last week's announcement that it is transforming into a "Physical AI solutions company," Hyundai Motor gave capacity figures at the San Francisco AI Summit: it will build a new humanoid robot factory in the US, targeting capacity of up to 30,000 units per year by 2028; location and investment amount remain undisclosed. This target was first floated in January when Boston Dynamics unveiled the production version of Atlas at CES; what's new this time is the capital and partnership details behind it — Hyundai bought out SoftBank's remaining stake in Boston Dynamics this month, ending the company's decade of changing hands three times (Google in 2013, SoftBank in 2017, Hyundai's roughly $880 million acquisition in 2021). The factory falls under Hyundai's $21 billion US investment commitment, of which $6 billion is earmarked for expanding partnerships with US companies. On rollout pace, Hyundai calls it "phased validation": Atlas will first keep pace with automotive assembly lines at its own factories (including the Metaplant America facility in Georgia), and only after that's proven will it be deployed externally or sold.
Source: How-To Geek source
LexisNexis patent ranking: The top five most innovative humanoid robot startups globally are all Chinese companies · humanoid
In a global humanoid robot startup innovation ranking published by legal research platform LexisNexis, Chinese companies swept the top five spots — Shanghai's Fourier Intelligence and Agibot, Shenzhen's LimX Dynamics and Pudu Robotics, and Hangzhou's Unitree — with Shenzhen's Engine AI (Chinese humanoid robot maker) ranking ninth, giving China six of the top ten. The US placed only Figure AI, Apptronik, and Agility Robotics at sixth, eighth, and tenth respectively, while Europe placed only Germany's Agile Robots at seventh. The ranking evaluates private startups that have demonstrated at least one bipedal walking robot, based on a patent asset index combining portfolio size, technical value, and geographic coverage. The report also notes that over the past decade, Chinese inventors' interactive-system patents have doubled, morphology-related patents have grown 2.5x, and control-and-planning architecture patents have grown fivefold; in morphology alone, China's share of global patent asset value has risen from 49% in 2020 to 63% in 2025, while the US share edged down from 12% to 11% over the same period. Source: South China Morning Post source
Former Tesla Houston FSD regional manager sues company, alleges safety staffing ratio was severely exceeded during testing · autonomy
Javier Medrano, former regional manager overseeing Tesla's Houston FSD testing operations, has filed a lawsuit alleging unlawful retaliation. The filing states that from October 2024 to May 1, 2025, the safety operator team under his management grew to 38 people, far exceeding the company's internal 1:15 management ratio standard, and that he repeatedly flagged insufficient staffing and operational resources without resolution; in one road-test incident response, a field operator was stranded at a high-risk location for an hour. He was terminated shortly after submitting a written report on the safety management gaps, and is now seeking reinstatement plus damages for forfeited equity and salary. ⚠️ Single-party account (the above are all allegations from the plaintiff's lawsuit; Tesla has not yet responded) Source: Sina Finance source
XPeng's AI Infra head Lu Siyuan reportedly departing for OpenAI Robotics · world-model
Lu Siyuan, head of XPeng Motors' AI infrastructure division, is reportedly handling departure handover, with his next stop being OpenAI Robotics; XPeng has neither confirmed nor denied this officially. He directly manages about 200 people, covering model training frameworks, GPU cluster scheduling, the compiler stack for XPeng's self-developed Turing chip, model quantization and compression, and in-vehicle inference runtimes — this chain is an unavoidable chokepoint for XPeng, which pursues a full-stack self-development path, since unlike automakers using Nvidia's stack, it can't get a packaged compiler and inference engine off the shelf. XPeng's response isn't to find a replacement but to split the 200-person team into three or more groups reporting separately, to reduce single-point dependency. Notably, the direction of talent flow has reversed: for the past decade, automakers poached talent from AI companies, but since Altman officially announced OpenAI's robotics business restart on June 1, OpenAI has been poaching automakers' self-developed-chip Infra heads at the intersection of "large-scale distributed training + extreme on-device deployment." ⚠️ Single-party account (the job-move information comes from a single media outlet; neither party has confirmed) Source: Robot Lecture Hall source
BYD discloses "Xiaodi" specs: 1.61m tall, 31 degrees of freedom, first units to work as store greeters · humanoid
BYD officially announced on July 28 that its self-developed humanoid robot "Xiaodi" will debut at its Zhengzhou Di Space showroom in early August (previously reported); this update adds the specific specs: height 1.61 meters, weight 58.5 kg, 31 degrees of freedom. BYD Executive Vice President Stella Li said the ideal setup is 2-3 units per store, freeing sales staff from repetitive basic explanations. The company also has an industrial prototype already undergoing internal testing at its own factories for tasks like material handling, inspection, and labeling.
Source: Wentu Finance source (WeChat, CN)
Hardware & Supply Chain
· Dexterous hand shipments and funding: Research firm data shows China's dexterous hand sales reached about 19,000 units in 2025, expected to exceed 70,000 units in 2026; funding scale has jumped in tandem — before 2026 rounds mostly hovered in the "tens of millions of yuan" range, but this year has already seen individual rounds near 1.5 billion, over 1 billion, and 500 million yuan source (WeChat, CN)
· Dekewei · Joint millimeter-wave wireless slip ring: Replaces the mechanical contacts of rotating interfaces with millimeter-wave wireless data transmission plus an electrical loop for power, supporting 10/100/1000 Mbps communication, expandable up to 3 Gbps in custom configurations, targeting a bit error rate below 10⁻¹², top rotation speed of 2000 r/min, and compatible with the hollow-shaft structure of existing integrated joints source
· Sapphire EDGE+ Apex: A robotics COM-HPC module plus carrier board based on the AMD Ryzen AI Embedded X100 series, up to 128GB LPDDR5x, aimed at consolidating perception, real-time control, and industrial reliability on an open x86 stack — a direct play for the robotics edge-compute market source
· Coreless motors: As the most common actuation method for dexterous hands, their miniaturization and power density directly determine how many degrees of freedom can fit into a single hand; China has already formed a supply base of 22 suppliers source (WeChat, CN)
Top comments (0)