The sim-to-real gap
Policies trained in simplified environments can fail when site conditions, materials, tools, or task execution change.
Multimodal AI data infrastructure for construction robotics
Training the future of the robotics workforce with real-world data.
Bridge the sim-to-real gap with multimodal training data. Agility Dynamics combines skilled human demonstrations, first-person vision, tactile sensing, and spatial motion in a unified pipeline for physical AI.
Agility Dynamics
Turning expert human work into structured data for the next generation of robotic systems.
The problem
Why construction robotics stalls: the physical AI data bottleneck.
Unstructured jobsites are difficult to model. Simulation cannot fully represent changing materials, human dexterity, tool contact, and unpredictable field conditions.
Robot developers need reliable multimodal demonstrations, but spatial vision, body motion, and contact signals are often collected in disconnected systems. Aligning that data is a costly obstacle between research and deployment.
Fragmented capture
Vision, tactile inputs, and spatial kinematics must be synchronized and calibrated before they become useful learning data.
Expert capture is hard
Translating trade expertise into machine-readable demonstrations demands practical sensing hardware, task context, and repeatable collection methods.
The platform
From skilled expertise to machine-ready physical AI data.
A hardware-agnostic capture architecture and robotics data pipeline connect four purpose-built systems: Agility Vision, Touch, Motion, and Intelligence.
01 · Agility Vision
Spatial vision capture
First-person visual sensing designed around protective jobsite equipment to capture tools, materials, spatial relationships, and task context.
- Task and tool visibility
- Scene and material context
- Perspective-aligned capture
02 · Agility Touch
Tactile and hand sensing
Instrumented gloves and tool interfaces for capturing finger articulation, contact, grip, and dexterous interaction during real work.
- Finger articulation
- Grip and contact signals
- Tool interaction capture
03 · Agility Motion
Spatial motion capture
Wearable kinematic capture for posture, joint movement, task sequences, and trajectories across unstructured work environments.
- Body motion and posture
- Task sequence reconstruction
- Human trajectory mapping
04 · Agility Intelligence
Robotics data pipeline
A data processing layer for time synchronization, annotation, task segmentation, and standardized outputs for imitation learning and simulation.
- Multimodal synchronization
- Action segmentation
- Robot-learning preparation
Multimodal captureVision · Touch · Motion
Capture layer
Capture expert work in real-world conditions.
Agility Dynamics captures first-person context, hand interaction, and whole-body movement in a single workflow. The objective is not just documentation. It is to preserve the structure of expert execution in a form machines can learn from.
Field-oriented sensingDesigned around real tools, materials, and task environments.
Multimodal alignmentEvery stream is synchronized so movement, perception, and task context remain connected.
Human expertise preservedWhat experienced workers know becomes a usable training asset instead of disappearing into unstructured footage.
Translation layer
From raw recordings to structured robot training data.
Raw recordings are only the starting point. Agility Intelligence reconstructs actions, segments tasks, and prepares data for simulation, evaluation, and downstream robotic learning workflows.
Action understandingIdentify meaningful sub-tasks, movement phases, and interaction patterns.
Structured outputsPrepare consistent representations that robotics teams can use across research and development pipelines.
Bridging human and machineCreate the connective layer between skilled work and repeatable machine capability.
Learning transferHuman expertise to machine execution
Built for construction
Real jobsites. Real constraints.
Construction robotics must account for people, tools, changing work zones, material tolerances, and safety-critical decisions. Better data captures more than motion. It preserves the conditions under which work is performed.
01 / JOBSITE CONDITIONS
Unstructured environments
Represent variable materials, changing geometry, nearby workers, and shifting task conditions.
02 / TASK QUALITY
Correct execution
Capture sequence, fit, contact, and the signals that distinguish a completed task from a failed attempt.
03 / SAFETY BY DESIGN
Hazards and boundaries
Incorporate hazard context and task constraints into dataset specifications and controlled robot evaluation. Protective eyewear must meet applicable jobsite PPE requirements.
The process
The sim-to-real robotics data pipeline.
Capture synchronized demonstrations, structure them for machine learning, and evaluate how policies transfer from simulation to variable, real-world conditions.
01
Capture
Record vision, touch, motion, and context during real skilled work.
02
Reconstruct
Turn multimodal recordings into structured demonstrations and action traces.
03
Translate
Prepare machine-readable representations for simulation and robotics training workflows.
04
Evaluate
Measure how demonstrations transfer into robotic behavior and iterate toward execution.
The vision
Real-world data for the next generation of physical AI.
Construction is the starting point. The broader opportunity is much larger: building the infrastructure that allows machines to learn from skilled human work across the physical economy.
Built through applied collaboration.
Agility Dynamics brings together the AI and systems expertise of Agility Automations with the field knowledge and construction experience of ACT Construction. The result is a practical, industry-informed approach to robotic learning infrastructure.
Partnerships
Move beyond simulation. Start with real-world expertise.
Developing a robotics training dataset, demonstration collection workflow, or sim-to-real research program? Connect with Agility Dynamics to discuss pilot collaboration and technical requirements.
Agility Dynamics · Developed through Agility Automations and ACT Construction