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Only First party#Model releases
May 8Fri
  1. Figure AI62

    Figure AI shows two Helix-02 humanoid robots collaborating to tidy a bedroom

    Figure AI demonstrated two humanoid robots equipped with Helix-02 collaborating to tidy a bedroom within two minutes, completing tasks such as opening a door, hanging clothes, putting away headphones, closing a book, taking out trash, pushing a chair under a desk, and making a bed together. The two robots run the same learned vision-language-action (VLA) policy, with no shared planner, message passing, or central coordinator; each infers the other's intent from its actions via its own cameras. The company says that to its knowledge this is the first demonstration of multi-humanoid collaborative mobile manipulation completed by a single learned neural network directly from pixels to actions.

    Why it matters: The official demonstration shows two humanoid robots driven by the same VLA system collaborating to tidy a bedroom, giving readers insight into an approach to multi-robot collaboration without a shared planner.

May 6Wed
  1. Genesis AI · YouTube32

    Genesis AI's GENE-26.5 Autonomously Performs Straw Flip at 1x Speed

    Genesis AI's robotic foundation model GENE-26.5 autonomously completed a continuous one-handed fine manipulation sequence: unwrapping a straw, flipping the straw with one hand, dropping it into a cup, and then handing the drink to a person. The entire sequence was executed autonomously and filmed at 1x speed, not teleoperated. The demo showcases fluid one-handed dexterous manipulation across multiple small, precise actions.

Apr 22Wed
  1. PsiBot/灵初智能75

    Lingchu Intelligence releases Psi-R2 and Psi-W0, open-sources 1,000-hour full-modality human hand manipulation dataset

    On April 10, Lingchu Intelligence released Psi-R2, pretrained on human data, and the Action-Conditioned World Model Psi-W0, and open-sourced the first 1,000 hours of a full-modality human hand manipulation dataset, with total data reserves reaching 100,000 hours.

    Why it matters: The original text provides the division of labor between the two new models and the open-source data entry point, so readers can assess the feasibility of using human data for robot training.

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 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.

Mar 20Fri
  1. 线性资本58

    Octopus Dynamics completes nearly $50 million first funding round, jointly invested by Horizon Robotics, Hillhouse Venture Capital, Xiaomi Strategic Investment, and others

    Physical AI company Octopus Dynamics (SynapX) announced the completion of a nearly $50 million first funding round. The investors are Horizon Robotics, Hillhouse Venture Capital, Xiaomi Strategic Investment, Shunwei Capital, and Linear Capital. The company was founded in January 2026, and less than two months passed from its founding to the closing of this first round.

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 3Tue
  1. Dexterity53

    Dexterity releases world model Foresight, unveils packing intelligence layer for production environments

    Dexterity has unveiled its world model Foresight, an intelligence layer that enables robots to reason about the physical world, predict changes, and execute operations; it was trained on real experience from more than 100 million autonomous actions in enterprise logistics production environments.

Feb 5Thu
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 7Wed
  1. 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.

Nov 19Wed
Nov 17Mon
  1. Physical Intelligence74

    Physical Intelligence releases π*0.6, a VLA model that learns from experience

    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.

Sep 26Fri
  1. Keenon Robotics/擎朗智能47

    KEENON Robotics officially releases KOM 2.0, a self-developed VLA model for the service industry

    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.

Sep 18Thu
  1. Figure AI72

    Figure launches Project Go-Big: Helix learns navigation zero-shot from human first-person video alone

    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.

Sep 3Wed
  1. Figure AI52

    Figure AI's Helix VLA model learns to load a dishwasher

    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.

Sep 2Tue
  1. LimX Dynamics48

    LimX Dynamics releases LimX DreamActor: a new embodied intelligence training paradigm combining Real2Sim2Real and real-robot reinforcement learning

    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.

Aug 12Tue
  1. Figure AI60

    Figure AI's Helix model achieves fully autonomous towel folding on a humanoid robot

    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.

Jun 7Sat
  1. Figure AI75

    Figure AI reports three-month Helix logistics deployment progress: 4.05 seconds per item, about 95% barcode success rate

    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.

Mar 25Tue
  1. Figure AI66

    Figure AI uses reinforcement learning to give Figure 02 humanoid robot a natural, human-like walk

    Figure AI released an end-to-end reinforcement learning walking controller that lets the Figure 02 humanoid robot learn a human-like gait in simulation by imitating human reference trajectories through rewards, including heel strike, toe push-off, and arm swing. Training runs in parallel across thousands of robots in GPU-accelerated physics simulation, collecting years of simulated data in hours, and combines domain randomization with kHz-level torque feedback control to achieve zero-shot transfer to the real robot; 10 Figure 02 units run the same neural network without individual tuning.

    Why it matters: The original text provides details on the training method and sim-to-real transfer for Figure 02's reinforcement-learning-based human-like walking, offering insight into its technical approach.

Jul 29Mon
  1. FieldAI48

    FieldAI develops first risk-bounded robot foundation model FFM using NVIDIA Isaac platform

    FieldAI claims to have developed the first risk-bounded Field Foundation Models (FFM) robot foundation model, which enables multi-form robots to operate autonomously in hazardous, off-road, and other unstructured industrial environments without prior maps, GPS, or cloud communication, and has already been deployed at hundreds of sites.