Workforce Academy and Applied Research

Engineering Talent for Physical AI

Hands-on laboratory training in GPU computing, AI infrastructure, ROS2, and NVIDIA Isaac simulation to equip engineers for modern robotics.

Pioneering Machine Intelligence

Pushing the frontiers of physical AI through rapid proof-of-concept validation, intelligent automation models, and experimental machine control.

A comprehensive end-to-end framework enabling machines to perceive, learn, and execute complex autonomous behaviors from synthetic simulation to edge hardware.

Data Generator

Skill Repository

Deployment Converter

Creates physically accurate SimReady assets and photorealistic synthetic datasets with rigorous domain randomization to train robust visual perception models.

Trains, benchmarks, and stores modular robotic skills utilizing NVIDIA Isaac Sim and Isaac Lab reinforcement learning frameworks across simulated industrial fleets.

Converts validated neural policies into optimized ROS2 micro-nodes and robot-specific executable packages engineered for real-time deterministic execution.

BREWS Middleware Bridge

BREWS serves as the critical connective tissue, translating high-level artificial intelligence and vision-language reasoning into low-latency control instructions for physical manipulators and mobile robots.

BREWS connects large language models and autonomous AI agents directly to physical machines. By translating technical operating manuals into verified robot execution routines, BREWS bridges digital cognition with industrial control.

Engineered for deep integration with ROS2, NVIDIA Isaac, and distributed edge hardware, enabling multimodal agents to safely command robotic actuators in production environments.

BREWS serves as InnovAI's embodied middleware, bridging high-level reasoning and physical industrial robots. It compiles engineering blueprints into deterministic machine instructions that execute safely across varied operational environments.

Reusable Instructions

Compiled skill definitions operate as hardware-agnostic control primitives, allowing proven assembly and inspection tasks to transfer seamlessly across differing robotic arm configurations without manual reprogramming.

Multi-Robot Integration

A unified communications backbone connects diverse kinematic systems—from multi-axis articulated arms to autonomous mobile bases—orchestrating collaborative physical tasks through synchronized ROS2 nodes and native OEM robot SDKs.

Manual to Knowledge Extraction

Skill Compiler to Instructions

Runtime to Physical Robot

Technical equipment manuals and engineering schematics undergo structured knowledge extraction, distilling operational constraints, safety tolerances, and component sequences into machine-actionable procedural graphs.

The skill compiler synthesizes extraction outputs into deterministic robot instructions, generating collision-free spatial paths and validated motor primitives decoupled from specific hardware kinematics.

The high-speed BREWS runtime streams optimized commands through standard ROS2 microservices and low-level robot SDKs, driving multi-axis physical robots with millisecond determinism on the live line.

Converts engineering blueprints, assembly manuals, and operational video feeds into validated OpenUSD assets and physics-accurate synthetic training datasets built specifically for NVIDIA Isaac Sim and Isaac Lab.

CAD, Manuals & Vision Feeds

SimReady OpenUSD Asset Compiler

NVIDIA Isaac Sim & Lab Datasets

Ingests raw parametric CAD models, equipment maintenance manuals, electrical schematics, and camera feeds to extract accurate physical dimensions, mounting points, and kinematic constraints.

Translates geometric files into SimReady OpenUSD assets with automated mesh optimization, mass-inertia calculation, joint actuation limits, and calibrated sensor-realistic material properties.

Outputs validated digital twins and photorealistic synthetic training datasets with procedural domain randomization to train robust robotic vision and manipulation policies at massive scale.

Precision Industrial Twin Generation

Eliminate sim-to-real transfer gaps before deploying code onto physical robots. Our automated pipeline generates millions of photorealistic sensor frames with dynamic lighting and physics perturbations.

Isaac Lab drives rapid policy acquisition through massively parallel reinforcement learning and imitation demonstrations. Complex physical behaviors are synthesized in digital twins, validated across severe domain variations, and deployed as reusable production skills.

10,000x

Parallel Sim Acceleration

Policy Training

Sim-to-Real Validation

GPU-accelerated Isaac Lab environments execute thousands of simultaneous rollouts, combining proximal policy optimization with human expert demonstrations to solve contact-rich manipulation tasks.

Continuous domain randomization alters mass, joint friction, and lighting parameters across parallel instances to guarantee zero-shot sim-to-real transfer before any physical hardware execution.

Zero-Shot

Hardware Transfer Fidelity

Semantic Versioning

Reusable Storage

Trained behaviors compile into optimized ONNX runtimes and containerized ROS2 nodes, stored in an open library ready for immediate drop-in hardware execution.

Every neural checkpoint records exact training seeds, simulation asset hashes, and convergence metrics in an auditable registry, ensuring deterministic rollbacks across robot fleets.

Machine Tending & Pick and Place

Trained for sub-millimeter part insertion, fixture alignment, and bin picking, these manipulation policies handle high-payload CNC machine tending and high-speed sorting without custom fixtures.

Inspection & Dynamic Navigation

Autonomous mobile manipulators leverage spatial SLAM and visual defect classification to traverse dynamic shop floors, inspecting structural welds and executing safety-critical patrol routes.

Enterprise Physical AI Solutions

Delivering reliable machine intelligence across factory floors, logistics facilities, and operational infrastructure.

Robotics Interfaces & Control

Industrial Agent Engineering

Hardware Systems Integration

Deploy adaptable machine interfaces and autonomous workflows that handle variable factory conditions without manual reprogramming.

Custom agentic pipelines that query operational telemetry, interpret sensor streams, and execute multi-step physical equipment tasks.

End-to-end engineering from proof-of-concept simulation to full hardware-in-the-loop deployment on active production lines.

Translating zero-shot neural policies into robust real-world motion requires rigorous runtime optimization, cross-platform middleware translation, and fail-safe hardware guards before sending raw motor commands.

1. Serialization & Quantization

2. LEAPP & Middleware Binding

3. SDKs & Safety Envelopes

Reinforcement learning checkpoints are stripped of training overhead and serialized via ONNX or TorchScript. Graph compilation and FP16/INT8 quantization ensure microsecond inference times on target edge silicon.

The LEAPP abstraction protocol packages state spaces into low-jitter ROS 2 nodes and Isaac ROS hardware acceleration packages, binding model observation inputs cleanly to physical lidar and optical sensor feeds.

Vendor-specific SDK interfaces enforce strict velocity clamp curves, torque-current limits, and real-time watchdog routines. If policy drift or sensor occlusion occurs, mechanical emergency halts engage in under 2ms.

Hardware-in-the-Loop Validation Protocol

Before field commissioning, compiled binaries undergo automated regression sweeps across kinematic URDF definitions and simulated latency injections, verifying that real joint trajectories match simulated safety tolerances.

Supported Targets: Universal Robots e-Series, Franka Emika Research 3, Unitree Go2 Quadruped, and NVIDIA Jetson Orin compute stacks running ROS 2 Humble.

Partner with InnovAI Solutions to build physical AI systems, deploy intelligent robotics, or upskill your engineering workforce.