The Challenge
Robotics teams are blocked at the data and training layer
Hardware advances faster than AI training infrastructure. Most robotics teams have the robot but not the data pipelines, annotation tooling, or evaluation frameworks to train models that work outside the lab.
Insufficient Training Data for Real Environments
Robots trained in simulation fail in production. Real-world variance — lighting, surface texture, object placement, human presence — creates a sim-to-real gap that collapses model performance. You need real interaction data, annotated at scale, with edge case coverage.
Multi-Sensor Annotation Complexity
Modern robots fuse LiDAR, RGB cameras, depth sensors, tactile feedback, and IMU data. Annotating multi-modal streams with temporal alignment, 3D bounding boxes, and semantic labels requires specialized infrastructure most teams lack.
Hardware-Specific Training Pipelines
A surgical robot, a warehouse picker, and an agricultural drone need fundamentally different training datasets, labeling schemas, and evaluation metrics. Generic data vendors can't serve hardware-specific requirements.
Safety Validation & Regulatory Gaps
Deploying AI-powered robots in medical, industrial, or public-facing environments requires safety case documentation, failure mode analysis, and compliance evidence for ISO 26262, IEC 61508, and FDA guidance. Most teams don't have this capability in-house.
Education AI Solutions
AI training services across every robotics hardware category
Industrial Robotics AI Training
Training data and perception AI for CNC robots, welding arms, assembly lines, and quality inspection systems. We build datasets covering component recognition, defect detection, precision manipulation, and multi-robot coordination — calibrated to your factory floor conditions.
Surgical & Medical Robotics
Training pipelines for surgical systems including da Vinci, CMR Surgical, and custom platforms. Tissue segmentation, instrument tracking, tool-tissue interaction data, and procedure-specific datasets with IRB-compliant data handling and FDA pre-submission support.
Warehouse & Logistics Robotics
Pick-and-place training data, bin detection, conveyor tracking, and autonomous mobile robot (AMR) navigation datasets. We support Boston Dynamics Stretch, Symbotic, Locus, and custom warehouse platforms with SKU-level object recognition training.
Agricultural & Field Robotics
Crop detection, disease identification, harvest readiness models, and terrain navigation training for agricultural robots. Multi-spectral imaging annotation, plant-level segmentation, and seasonal variance datasets for John Deere, FarmWise, and custom agri-bot platforms.
Humanoid & Service Robotics
Whole-body manipulation training, human-robot interaction datasets, dexterous hand grasping libraries, and locomotion evaluation for humanoid platforms including Figure, Agility Robotics, Unitree, and Boston Dynamics Atlas.
Autonomous Drone & Aerial Robotics
LiDAR, camera, and radar annotation for inspection drones, delivery UAVs, and autonomous aerial platforms. Obstacle detection, landing zone identification, and payload handling training datasets compliant with FAA beyond visual line of sight (BVLOS) requirements.
Defense & Security Robotics
Training data for autonomous ground vehicles (UGVs), explosive ordnance disposal (EOD) robots, perimeter security systems, and surveillance platforms. MIL-STD compliant pipelines with ITAR-aware data handling and secure annotation environments.
Collaborative Robot (Cobot) Training
Human-robot interaction safety datasets, proximity detection training, force-torque sensor fusion, and shared workspace models for Universal Robots, FANUC CRX, ABB YuMi, and KUKA iiwa cobot platforms.
End-to-End Implementation
How Solnix trains your robotics AI end-to-end
Every robotics engagement follows the same proven lifecycle — from hardware audit through production model delivery. We adapt the methodology to your sensor stack, platform, and deployment environment.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Methodology
How Solnix Builds Robotics Training Infrastructure
01, Hardware & Sensor Stack Audit
01, Hardware & Sensor Stack Audit
We start with a deep audit of your robot's sensor configuration — camera specs, LiDAR beam density, tactile arrays, force-torque sensors, IMU placement. This determines the annotation schema, data collection protocol, and fusion architecture required for your specific platform.
02, Training Data Collection Design
02, Training Data Collection Design
We design real-world data collection protocols: which environments to capture, what task demonstrations to record, how to structure failure cases, and what sim-to-real transfer strategy to use. For hardware not yet in the field, we design simulation pipelines in Isaac Sim, Gazebo, or custom environments.
03, Multi-Modal Annotation Pipeline
03, Multi-Modal Annotation Pipeline
Our annotation team builds hardware-specific pipelines with specialized labelers for each sensor modality. 3D bounding boxes, semantic segmentation, keypoint annotation, trajectory labeling, and tactile interaction tagging — all temporally aligned across sensor streams.
04, Edge Case & Safety Scenario Coverage
04, Edge Case & Safety Scenario Coverage
We systematically engineer edge cases: occlusion scenarios, sensor degradation, unexpected object types, human interference, hardware faults. Safety-critical robotics requires 3–5× oversampling of rare scenarios to ensure model robustness at the tails.
05, Model Evaluation & Safety Validation
05, Model Evaluation & Safety Validation
Every model trained on our data gets evaluated against hardware-specific benchmarks, worst-case scenario test suites, and regulatory requirements. We produce safety case documentation for ISO 26262, IEC 61508, ISO 10218, and FDA guidance where applicable.
FAQ
Questions from robotics engineers, CTOs, and hardware teams
Get Started
Your Hardware Is Ready. Let's Train the AI.
Solnix builds the training data infrastructure, annotation pipelines, and AI evaluation systems that transform your robotics hardware into autonomous, reliable systems — across every platform, sensor stack, and deployment environment.
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