Physical Intelligence requires Physical Data
Physical Intelligence requires Physical Data
Physical Intelligence requires Physical Data
We built this missing data layer. The world’s most physics-rich human demonstration dataset that fuses vision with the invisible forces, interactions, and inertial measurements of the real world, enabling robotics models to finally scale from "seeing" to "understanding."
the problem
We need the "Data Vault" for Physical Intelligence
the problem
We need the "Data Vault" for Physical Intelligence
the problem
We need the "Data Vault" for Physical Intelligence
Physical intelligence has not yet scaled like LLMs, a major bottleneck is the data that powers the models.
Physical intelligence has not yet scaled like LLMs, a major bottleneck is the data that powers the models.
LLMS
LLMS
Scaled because the internet provided trillions of tokens of text (logic + language).
VLMS
VLMS
Scaled because the internet provided billions of hours of video (pixels + observation).
VLAs
VLAs
Cannot embody today’s robotics hardware without high fidelity, physics-rich human demonstration data.
LLMS
Scaled because the internet provided trillions of tokens of text (logic + language).
VLMS
Scaled because the internet provided billions of hours of video (pixels + observation).
VLAs
Cannot embody today’s robotics hardware without high fidelity, physics-rich human demonstration data.
THE GAP
Why Current Human Demo Data Quality is Not Enough
THE GAP
Why Current Human Demo Data Quality is Not Enough
THE GAP
Why Current Human Demo Data Quality is Not Enough
Current state-of-the-art datasets (Open X-Embodiment, Egocentric 10k, DROID) rely on two main modes:
egocentric video
egocentric video
Head mounted camera or VR headset. While useful for high-level planning, this modality hits a hard ceiling for manipulation. I.E. Egocentric 10k.
teleoperation
teleoperation
Human operator remotely control or action sync with a robot with an end effector of reduced degree of freedom. I.e. UMI, Open X.
Current state-of-the-art datasets (Open X-Embodiment, Egocentric 10k, DROID) rely on two main modes:
egocentric video
Head mounted camera or VR headset. While useful for high-level planning, this modality hits a hard ceiling for manipulation. I.E. Egocentric 10k.
teleoperation
Human operator remotely control or action sync with a robot with an end effector of reduced degree of freedom. I.e. UMI, Open X.
Both of the methods are not hardware future-prone, as the degree-of-freedom increases, gesture mapping becomes increasingly difficult. At the moment, they only provide intermediate steps towards true physical intelligence.
lack of physics
lack of physics
No physical interaction information recorded, poor pose estimation and 3D scene reconstruction through egocentric video feed. All these contribute to poor transferability.
imitation
imitation
Models trained on data can only be as good as the data is. This sets a pessimistic ceiling of how good the models will be trained on the teleoperation dataset.
We capture the "invisible" data. By fusing high frequency inertial data (IMU), physical forces (wearable force sensors) with 3D Volumetric Point Clouds, we provide the ground truth for kinematics and dynamics of poses.
Both of the methods are not hardware future-prone, as the degree-of-freedom increases, gesture mapping becomes increasingly difficult. At the moment, they only provide intermediate steps towards true physical intelligence.
lack of physics
No physical interaction information recorded, poor pose estimation and 3D scene reconstruction through egocentric video feed. All these contribute to poor transferability.
imitation
Models trained on data can only be as good as the data is. This sets a pessimistic ceiling of how good the models will be trained on the teleoperation dataset.
We capture the "invisible" data. By fusing high frequency inertial data (IMU), physical forces (wearable force sensors) with 3D Volumetric Point Clouds, we provide the ground truth for kinematics and dynamics of poses.
THE TECHNOLOGY
Industrial-Grade Infrastructure
THE TECHNOLOGY
Industrial-Grade Infrastructure
THE TECHNOLOGY
Industrial-Grade Infrastructure
the hardware
We partner with industry leading motion-capture companies with 10+ years track records to deliver the best hardware options possible.
vision
vision
Multi-view RGB + RGB-D (Depth/Volumetric).
physics
physics
Finger-mounted IMUs on a wearable glove assembly captures fine-grained vibration and motion dynamics, while refining pose estimation accuracy.
sync
sync
Auto hardware triggers and key point detection, ensuring multimodal synchronization.
the hardware
We partner with industry leading motion-capture companies with 10+ years track records to deliver the best hardware options possible.
vision
Multi-view RGB + RGB-D (Depth/Volumetric).
physics
Finger-mounted IMUs on a wearable glove assembly captures fine-grained vibration and motion dynamics, while refining pose estimation accuracy.
sync
Auto hardware triggers and key point detection, ensuring multimodal synchronization.
THE SOFTWARE
We deliver cleaned, accurately labeled, and sliced to trajectory dataset via a proprietary post-processing pipeline.
post processing
post processing
Pose estimation, point cloud generation, labeling, and slicing.
Metric Scoring
Metric Scoring
Automated pre-scoring of consistency, path optimality, and task completeness.
We operate a proprietary capture and cleaning pipeline designed for high-precision imitation learning, deployed in real-world factories rather than sterile labs.
THE SOFTWARE
We deliver cleaned, accurately labeled, and sliced to trajectory dataset via a proprietary post-processing pipeline.
post processing
Pose estimation, point cloud generation, labeling, and slicing.
Metric Scoring
Automated pre-scoring of consistency, path optimality, and task completeness.
We operate a proprietary capture and cleaning pipeline designed for high-precision imitation learning, deployed in real-world factories rather than sterile labs.
THE PRODUCT
most curated, physics-rich, scalable human demonstration dataset
THE PRODUCT
most curated, physics-rich, scalable human demonstration dataset
THE PRODUCT
most curated, physics-rich, scalable human demonstration dataset
We function as the TSMC of robotics data—a neutral foundry powering the ecosystem.
We function as the TSMC of robotics data—a neutral foundry powering the ecosystem.
a new benchmark
A gold-standard, human expert demonstration dataset with a wide range of task diversity. Designed to outperform video-only baselines and serve as the industry reference, scaled with both high volume and great expertise.
a new benchmark
A gold-standard, human expert demonstration dataset with a wide range of task diversity. Designed to outperform video-only baselines and serve as the industry reference, scaled with both high volume and great expertise.
value proposition
why us, why now
value proposition
why us, why now
value proposition
why us, why now
Deep Moat
Deep Moat
Our team owns the supply (factory access), the process (cleaning pipeline), and customers (cooperate clients from top AI labs); and we are creating the new SOTA standard (physics-rich schema).
Scalability
Scalability
There aren’t many ways a dataset can scale in labs. Unlike lab-based collection, our distributed factory workbench hardware scales across global industrial sites, capturing the diversity of the real world while ensuring an incomplete data volume.
Timing
Timing
Hardware is approaching human capability; VLA models are ready and data-hungry; Data is the only missing layer. It’s a classic egg or chicken problem. Once the new dataset is there, the research direction aligns.
Deep Moat
Our team owns the supply (factory access), the process (cleaning pipeline), and customers (cooperate clients from top AI labs); and we are creating the new SOTA standard (physics-rich schema).
Scalability
There aren’t many ways a dataset can scale in labs. Unlike lab-based collection, our distributed factory workbench hardware scales across global industrial sites, capturing the diversity of the real world while ensuring an incomplete data volume.
Timing
Hardware is approaching human capability; VLA models are ready and data-hungry; Data is the only missing layer. It’s a classic egg or chicken problem. Once the new dataset is there, the research direction aligns.
