[ STABLEBROWSE / PHYSICAL AI DATA INFRASTRUCTURE ]
Backed by
The data infrastructure
layer for physical AI.
We build multimodal physical-world datasets for robots and embodied agents, from real task capture to post-processed depth, hand pose, tactile signals, camera motion, action labels, and reviewable training outputs.
[ 01 / THESIS ]
Physical AI needs an infrastructure layer, not scattered clips.
The internet does not contain enough examples of hands using tools, objects changing state, bodies moving through space, and people recovering from messy physical interactions. Physical AI needs data collected in the world, not just scraped from it.
StableBrowse handles the full data layer: capture protocol, sensor synchronization, calibration, depth, hand tracking, hand mesh, camera trajectory, tactile streams, temporal labels, and reviewable delivery.
[ 02 / CAPABILITIES ]
From raw physical capture to model-ready data.
01
Multimodal capture
We collect real physical tasks with egocentric RGB, stereo depth, IMU, audio, hand pose, tactile gloves, tool state, and environment context.
02
Post-process
We turn raw recordings into aligned outputs: depth, hand tracking, hand mesh, camera trajectory, object state, action boundaries, and sensor QC.
03
Validate and deliver
Every episode ships with synchronized timestamps, calibration, schema-valid labels, review UI assets, manifests, and provenance your team can audit.
[ 03 / THE STANDARD ]
Post-processing is
the product.
We do not just collect footage. We convert multimodal capture into structured signals that a model team can actually use: aligned sensor data, calibrated geometry, physical action labels, and artifacts that can be inspected frame by frame.
[ 04 / TRUST ]
Built for multimodal physical data.
Before StableBrowse, co-founder Jay Mehta co-authored MDD, an ICCV 2025 paper introducing a benchmark multimodal dataset for text-controlled and music-conditioned 3D duet dance generation.
MDD comprises 620 minutes of high-quality motion-capture data performed by professional dancers, synchronized with music, and detailed with over 10K fine-grained natural-language descriptions. That same discipline in synchronized sensors, motion understanding, and dense human-readable labels is what we bring to physical AI data infrastructure.
We design collection protocols for embodied tasks, then turn raw capture into structured training data: video, stereo depth, IMU, tactile gloves, hand pose, hand mesh, camera motion, object state, and temporal captions aligned to the task.
[ 05 / DATA LAYER ]
Multimodal physical capture
First-person recordings of real people doing real physical work, captured with RGB, stereo depth, IMU, audio, tactile gloves, hand pose, and tool context.
Manipulation and tool-use episodes
Fine-grained hand-object interaction data for cooking, assembly, packaging, sewing, measuring, machine work, inspection, and other embodied workflows.
Temporal action labels
Dense segment boundaries and captions for physical subtasks, including grasp, lift, move, place, adjust, inspect, recover, and repeat.
3D post-processing
Depth, hand tracking, hand mesh, camera trajectory, calibration, object state, and IMU traces packaged alongside the source video.
Reviewable annotation layer
Model-assisted annotations reviewed by trained labelers, with schema validation, time-synced review pages, traceable JSON, and output manifests.
Custom data programs
If your model needs a physical skill, environment, tool, object class, or sensor stack, we build the collection and post-processing protocol around it.
[ 06 / CONTACT ]
Tell us the physical skill
your model needs.
We will define the sensor stack, collect the episodes, post-process the signals, and deliver the dataset in a format your training pipeline can consume.