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#Figure AI
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.

Apr 29Wed
  1. Figure AI64

    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.

    Why it matters: The official figures give specific production and quality-control numbers and explain how fleet scale supports the deployment of perception-conditioned whole-body control.

Mar 18Wed
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.

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.

Nov 19Wed
  1. Figure AI78

    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.

    Why it matters: The manufacturer disclosed specific KPIs and hardware failure improvements from the 11-month production line deployment, allowing readers to understand the actual operating conditions of humanoid robots on automotive production lines.

Oct 9Thu
  1. Figure AI67

    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.

    Why it matters: The official announcement fully explains the new-generation humanoid robot's perception, touch sensing, home-safety design, and mass-production plans, so readers can understand its hardware changes and production capacity targets.

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 17Wed
  1. Figure AI69

    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.

    Why it matters: The official partnership announcement covers three areas: data scale, infrastructure, and deployment, letting readers understand how the data sources support scaling Helix.

Sep 16Tue
  1. Figure AI74

    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.

    Why it matters: Figure's official disclosure of the funding size and how it will be used lets readers understand the priorities for advancing its three lines: Helix, BotQ manufacturing and data collection.

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.

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.

Jul 17Thu
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.