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Figure AI

Updates on Figure AI's robotics products, models, research, funding, applications, and partnerships.

16 selected · 0 in the last 30 days

  1. Figure AI ·

    Figure AI releases Helix 2.5, achieving zero-shot full-body autonomy in 30 unfamiliar homes

    Figure AI released Helix 2.5, which completed three long-horizon full-body behaviors—tidying a living room, folding towels, and making a bed—zero-shot in 30 Bay Area homes where no data had ever been collected, without collecting data or fine-tuning in the evaluation environments.

  2. Figure AI ·

    Figure and Nscale Sign Strategic Agreement to Deploy Up to 100,000 NVIDIA Vera Rubin Platform GPUs

    Figure announced a strategic partnership with Nscale to deploy up to 100,000 GPUs on the NVIDIA Vera Rubin platform, with initial deployment targeted to begin in Barstow, Texas, in the second half of 2027, an initial compute commitment of $3.5 billion and intent to expand to more than $6 billion.

  3. Figure AI ·

    Figure AI launches Index app to build large-scale, diverse robot training dataset

    Figure AI announced it is exiting stealth mode and launching the Index app on Google Play and the App Store to collect real-world physical data from Creators worldwide to train its Helix AI system.

  4. Figure AI ·

    Figure 03 arrives at BMW Spartanburg plant for sequencing sorting tasks

    Figure AI announced that the Figure 03 humanoid robot has entered Hall 52 at BMW Group Plant Spartanburg, shifting from the sheet metal loading and unloading work previously done by Figure 02 to a sequencing sorting use case; Figure 02 took part in assembling 30,000 vehicles last year.

  5. Figure AI ·

    Figure AI Signs Agreement with Catalyst Brands to Deploy Humanoid Robots Across Its Logistics Network

    Figure AI announced a commercial agreement with Catalyst Brands to deploy Figure humanoid robots across its distribution and logistics network, starting at the Reno, Nevada distribution and logistics center, focusing on high-intensity physical tasks in automated supply chains.

  6. Figure AI ·

    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.

  7. Figure AI ·

    Figure 03 production ramps up: BotQ capacity rises 24-fold in 120 days to 1 unit per hour

    Figure announced that its BotQ factory has delivered more than 350 third-generation humanoid robots, Figure 03, with takt time improving from 1 unit per day to 1 unit per hour, a 24-fold throughput increase in 120 days. The line is driven by self-developed manufacturing execution software and covers more than 150 networked workstations. First-pass yield for completed units exceeds 80%, the battery line's first-pass yield is 99.3%, more than 9,000 actuators have been produced, and each robot undergoes more than 80 functional verification tests before leaving the factory.

  8. Figure AI ·

    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.

  9. Figure AI ·

    Figure 02 completes 11-month run at BMW Spartanburg plant, contributing to over 30,000 X3 vehicles, officially begins retirement

    Figure AI released results from Figure 02's 11-month deployment at BMW Group Plant Spartanburg: it ran 10-hour shifts each workday, loaded over 90,000 sheet metal parts, accumulated over 1,250 operating hours, contributed to the production of over 30,000 X3 vehicles, and the robot walked about 1.2 million steps, totaling over 200 miles.

  10. Figure AI ·

    Figure AI releases third-generation humanoid robot Figure 03

    Figure AI has released its third-generation humanoid robot, Figure 03, redesigned from the ground up for the Helix VLA model, the home, and large-scale mass production, covering both software and hardware.

  11. Figure AI ·

    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.

  12. Figure AI ·

    Figure AI and Brookfield Announce Strategic Partnership to Build Humanoid Robot Pretraining Dataset

    Figure AI announced a strategic partnership with Brookfield. Brookfield, which manages over 1 trillion USD in assets and owns more than 100,000 residential units, will help Figure build a real-world humanoid robot pretraining dataset, build the AI infrastructure needed to scale Helix (Figure's in-house vision-language-action model), and explore commercial deployment of humanoid robots.

  13. Figure AI ·

    Figure AI closes over $1 billion Series C at $39 billion post-money valuation

    Figure AI announced over $1 billion in committed capital for its Series C round at a $39 billion post-money valuation, led by Parkway Venture Capital with participation from NVIDIA, Brookfield, Intel Capital, Salesforce, Qualcomm Ventures and others.

  14. Figure AI ·

    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.

  15. Figure AI ·

    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.

  16. Figure AI ·

    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.