Beyond Vision
Synchronized vision, stereo depth, tactile force, IMU, and wrist POV teach policies how the physical world actually behaves.
Robotics
Train embodied AI with multimodal, human demonstration data
Multimodal data for VLA training, imitation learning, and policy refinement at scale
The path to embodied intelligence starts with real human physics and activity data.
Pixels show what the world looks like — multimodal capture shows how it moves, twists, grips, and resists, the signals robots need to act, not just see.
Tridi delivers vision, depth, tactile, IMU, and structured 3D outputs across diverse real environments so policies generalize across embodiments and tasks.
Why robotics labs need real human physics data
Diverse Environments
Capture spans homes, retail, industrial sites, and outdoor settings so your policies generalize beyond toy scenes to the real world.
Structured 3D Outputs
Depth maps, body pose, MANO hand reconstruction, and action labels ship in research-grade formats your training pipeline ingests directly.
Our approach
End-to-end policy training loop
Tridi captures the multimodal data your team needs at every stage of the policy lifecycle: from broad pre-training to task-specific fine-tuning to real-world reinforcement.
Human Demonstrated Data
Structured POV and third-person recordings of real people performing real-world tasks, captured with consistent hardware and QC for embodied learning.
Robot Teleoperation Data
Multi-view teleop sessions from humanoids we operate, designed to generate clean manipulation trajectories for control and imitation learning.
Annotated Action Segmentation
Fine-grained, multi-stream action labeling with natural-language descriptions in JSONL/CSV for VLA and policy training.
Training pipeline
Stage 01
Pre-train on human demonstrations
Bootstrap broad priors from millions of POV and third-person task recordings.
Stage 02
Fine tune with teleoperation data
Sharpen control policies with clean trajectories from teleop sessions on the target embodiment.
Stage 03
Reinforce with real-world feedback
Close the loop with annotated rollouts and human-in-the-loop preference data from live deployments.
Engagement Framework
We'll provide the multimodal rigs, operators, structured 3D outputs, and pipelines to turn real human activity into research-grade datasets.
01
Scope
Define the tasks, embodiments, and multimodal signals your policies require
02
Capture
Operators record synchronized vision, depth, tactile, and IMU across environments
03
Deliver
Ship structured 3D outputs with depth, body pose, and MANO hands
Result
Research-grade multimodal datasets with structured 3D outputs tailored to your robotics training pipeline
Example Use Cases
VLA Model Training
Multimodal action segmentation with natural-language descriptions for VLA policy training.
Imitation Learning
Real human teleop with tactile and 3D pose for dexterous control and object interaction.
Environment Libraries
Task libraries from homes, retail, factories, and outdoor sites — real lived-in environments.
The data infrastructure for physical AI breakthroughs