1X Technologies disclosed that the AI development process for its humanoid robot NEO is based on NVIDIA's robotics platform, covering the open-source physical AI agent tools released this week at GTC Taipei.
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.
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.
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.
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.
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.
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.
Humanoid has released KinetIQ, a proprietary AI framework for end-to-end unified orchestration of humanoid robot fleets in industrial, service, and home scenarios; a single system can control robots with different body and end-effector forms.
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.
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.
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.
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.
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.
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.
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.
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.
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.
By fine-tuning its latest π 0.6 model, Physical Intelligence achieved preliminary solutions for the gold-medal tasks in 3 of the five everyday manipulation challenges in the Robot Olympics proposed by Benjie Holson, and reached silver-medal level in the remaining 2, with an average success rate of 52% and task progress of 72%.
Why it matters: Using the same set of externally proposed everyday manipulation tasks to compare performance with and without robot pretraining lets readers judge the practical value of foundation-model pretraining for manipulation skills.
Physical Intelligence research found that in generalization scenarios where only human demonstrations appear, fine-tuning π0.5 with human first-person videos mixed into robot data alone improved policy performance by about 2x, without using any explicit transfer learning mechanism.
Why it matters: Experiments show that merely mixing human videos into fine-tuning improves the policy by about 2x on scenarios with purely human demonstrations, and this capability emerges as pretraining robot data scale grows.
Sunday Robotics released the foundation model ACT-1, claiming it was trained without any robot manipulation data; instead, human motions were captured with a Skill Capture Glove and converted into robot data via Skill Transform with a 90% success rate.
Physical Intelligence released the VLA model π*0.6. After training with its Recap (RL with Experience & Corrections via Advantage-conditioned Policies) method, throughput on some of the hardest tasks more than doubled, and the failure rate can be reduced to half of the original or lower.
Why it matters: The original article explains how compounding errors arise in VLA under imitation learning and presents a training approach that uses expert corrections and autonomous experience for reinforcement learning.
Dexterity proposes "Transactable World Models," which implement spatial understanding as a shared operator subject to physical constraints; multiple asynchronous skill agents interact with it transactionally, providing uncertainty bounds, rollback, interpretability, and real-time consistency.
Holiday Robotics has unveiled Friday, an industrial humanoid robot whose hands use a 500-gram, 20-DOF hydraulic ultralight design, with about 80% of their surface covered by self-made tactile sensors that can sense a force of 0.01N (about 1g), as well as direction and torque.
At CeMAT ASIA 2025, Sainade made its debut showcasing the upgraded linkage of iLoabot-M and iLoabot-X, with a live on-site demonstration of the full process of "loading and unloading—depalletizing and palletizing—transfer," in which the two machines worked in coordination to complete an unmanned closed-loop operation.
Diligent Robotics announced the launch of Moxi 2.0, which the company says delivers 10 times the compute of Moxi 1.0 and is built on the NVIDIA Thor platform. The platform was trained on three years of real-world hospital deployment data and more than 1.25 million deliveries; Moxi currently delivers medications and lab samples for nurses and pharmacy staff at more than 25 hospitals across the U.S.
Xinyichang has officially released the first-generation HOSON-Robot embodied intelligent humanoid robot and will proceed with subsequent small-batch mass production. The robot features the self-developed RMC100 "cerebellum" motion controller, supporting synchronized drive of 64 joint motors with synchronization error below 100ns and a control frequency of 4kHz; its model adopts a hybrid intelligence architecture that integrates a VLA end-to-end model with a fast-slow dual-system hierarchical model, and its self-developed dexterous hand and lightweight joint modules have completed preliminary adaptation for industrial scenarios.
Tashi Intelligence has released World In Your Hands (WIYH), described as the world's first large-scale real-world embodied VLTA (vision-language-tactile-action) multimodal dataset, and plans to share it with the industry in December 2025.
Tianlian Robotics has published a video imitation learning training pipeline: extract reference motions with GVHMR/TRAM (or use motion capture datasets such as AMASS), then after human-to-robot joint mapping and training a motion orientation model, use PPO reinforcement learning in simulation with curriculum learning to progress from static poses to long-sequence complex motions, then optimize through domain randomization before deploying to the real robot, with fine-tuning via residual reinforcement learning or adaptive gains. The company says it has achieved key breakthroughs in dynamic force-control precision and stability, and that motions can also be reproduced in real robotic arm operation.
Sainade showcased its full iLoabot lineup of three robots, iLoabot-M, iLoabot-P, and iLoabot-X, at the 2025 China International Industry Fair (CIIF), with live demonstrations of truck loading and unloading and slope climbing and obstacle crossing. iLoabot-X is its first heavy-duty dual-arm humanoid robot, built on Sainade's self-developed VLA, the logistics industry's first vertical large model, with an initial positioning time of 5 minutes and full-machine production in 30 minutes; the company says the iLoabot series has already been deployed at scale by leading enterprises in tobacco, beverages, and express delivery.
KEENON Robotics has officially released KOM 2.0 (KEENON Operator Model 2.0), which it calls the world's first VLA (vision-language-action) model for the service industry. The model uses a dual-system architecture with the K-Mind thinking system and the K-Act action system, is trained on hundreds of millions of real service interaction data points, and supports millisecond-level real-time decision-making.
The paper "Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-top Manipulation," a collaboration between SUSTech CLEAR Lab, LimX Dynamics, and the University of Hong Kong, has been accepted to CoRL 2025.
Why it matters: This research presents a concrete technical approach and experimental figures for learning manipulation from human video, helping readers understand the feasibility of training robot manipulation with video data.
Figure announced two advances in Project Go-Big: a partnership with Brookfield to build a large-scale humanoid pretraining dataset, with Brookfield holding over 100,000 homes, 500 million square feet of commercial office space, and 160 million square feet of logistics space; and Helix, after training on 100% first-person human video, responding to natural-language commands such as "go to the fridge" without any robot demonstrations, navigating cluttered real home environments.
Why it matters: The original gives the training method by which Helix learned navigation using only human video and the scale details of the Brookfield data partnership, offering a view of actual progress on the humanoid pretraining data approach.
Figure AI announced that its VLA (vision-language-action) model Helix learned to load a dishwasher using only new data, without changing the algorithm or adding special engineering. Helix can separate stacked plates and load them in order, pick up a glass with one hand, reorient it, and carefully place it with the other hand; it can also handle cluttered starting states and recover from grasp failures or collisions. The same model previously completed towel-folding and package-sorting tasks, and the company says this reflects the direction of a single general system learning new capabilities incrementally with data.
LimX Dynamics has introduced LimX DreamActor, an embodied intelligence training paradigm that for the first time deeply integrates Real2Sim2Real, imitation learning, and real-robot reinforcement learning into a full pipeline of "real-world environment data collection—simulation data pretraining—real-robot data post-training." The system can complete multi-view data collection using only a consumer-grade camera; through 3D reconstruction and physical parameter alignment it builds a highly realistic simulation scene, then uses a small amount of real-robot reinforcement learning to complete cross-domain transfer, aiming to improve sample efficiency, convergence speed, and deployment stability.
The 2025 World Robot Conference concluded on August 12. Estun Cuzhuo exhibited the CODROID02 humanoid robot and the Co-Palletizer 20 lifting palletizing system. CODROID02 stands 170 cm tall, weighs 70 kg, has a single-arm payload of 5 kg, and has 31 degrees of freedom across its body (excluding dexterous hands). Its joint modules are fully self-developed, and it uses a fast-slow system architecture combining a vision-language large model with a flow matching strategy. On site, it demonstrated part grasping and barcode scanning.
Figure AI's VLA model Helix fully autonomously folds towels, which the company says is the first time a multi-fingered humanoid robot has completed this task using an end-to-end neural network. With the architecture and training hyperparameters unchanged and only a new dataset added, it can pick items from a mixed towel pile, adjust its folding strategy based on the initial shape, correct multiple grasping errors, and untangle twisted towels using fine motions such as tracing edges with its thumb and pinching corners.
Why it matters: The same model completes the towel-folding task with no architecture changes, relying only on added data, demonstrating a generalization path from logistics to deformable object manipulation.
Sainade's heavy-duty dual-arm autonomous working robot iLoabot-X made its debut at the World Robot Conference (WRC), demonstrating on-site dual-arm collaborative handling of a cumulative 40 kg of cargo to complete transfer, as well as autonomous recognition of cage cart positions and path planning to push them. Its autonomous working capability comes from Sainade's self-developed multimodal VLA vertical large model, the first in the logistics industry, which parses operational environment details through multimodal perception. The same series, iLoabot-M, has already been running stably in the tobacco, alcoholic beverage, and express logistics sectors, with the company claiming a 300% efficiency improvement over traditional solutions.
SAIC Beijing and LimX Dynamics signed a strategic cooperation memorandum in Beijing, announcing the establishment of a joint embodied intelligence laboratory to conduct embodied robot algorithm R&D, system development, and toolchain deployment around automotive scenarios. LimX Dynamics will provide underlying technologies including robot bodies, whole-body motion control algorithms, VLA model frameworks, data engines, and toolchain platforms. The two parties will carry out humanoid robot data collection, training, and evaluation in automotive manufacturing scenarios, and gradually form a complete R&D process from data collection and model training to system testing.
On June 11, Estun Cobot officially launched its second-generation humanoid robot, Codroid 02, at the 2025 Intelligent Robot Development Conference. It stands 170 cm tall, weighs 70 kg, has a single-arm payload of 5 kg, and has 31 degrees of freedom across its body (excluding dexterous hands), with all core components independently developed in-house.
Figure AI released progress on Helix's capabilities after three months of deployment in a logistics environment: per-item processing time fell from about 5.0 seconds to 4.05 seconds, barcode scanning success rate rose from about 70% to about 95%, and the system gained the ability to handle flexible packages such as poly bags and flat envelopes.
Why it matters: The original article provides the data scale and ablation experiment details for each architecture module, allowing readers to understand the specific sources of the logistics performance improvement.