AI Systems for Industrial Robotics: Perception, Motion Planning and RL

Our AI robotics system integrates computer vision robots with 6DoF pose estimation and bin picking, delivering 91% success. Industrial manipulator with classical trajectory planning performs one task in 0.3 seconds reproducibly, but adapting to new objects requires hours of reprogramming. The same arm with an ML perception + grasp planning system picks an arbitrary unknown object from a bin with 91% success rate on the first try — a qualitative leap. The gap between programmable automation and AI robotics is exactly here. We specialize in implementing such systems: our experience includes 5+ years in industrial robotization and 30+ projects for mechanical engineering, logistics, and electronics. A typical project pays for itself in 8–14 months by reducing defects and changeover time. For example, for one electronics manufacturer, implementing RL-based peg-in-hole reduced defects by 40% and led to annual savings of $120,000. We guarantee a 6-month post-implementation support and our team has 5+ years of experience. Contact us for an assessment of your project — we will select the optimal solution. Request a production audit to find out exact timelines and development cost for your task.

How an AI system solves the bin picking problem

6DoF Pose Estimation

To grasp an object, the manipulator must know exact position and orientation (6 degrees of freedom). RGB-D camera (Intel RealSense D435, Azure Kinect) + RGBD dataset of specific parts. Methods:

• FoundationPose (NVIDIA): universal model, works from 1 reference image or CAD model without fine-tuning. Accuracy: <5 mm translation, <5° rotation on YCBv dataset.
• Training from scratch: Dope (Deep Object Pose Estimation) or GDR-Net — more accurate on specific parts, requires synthetic dataset with domain randomization (BlenderProc).

Domain gap is the main problem: model trained on synthetic data, deployed in real factory lighting. Domain randomization (random textures, lighting, backgrounds) + a small real-world fine-tuning solves the problem in 200–500 real annotated frames.

Bin Picking with 3D point cloud

Grabbing parts from an unordered bin: Open3D + PointNet++ for segmenting individual parts in point cloud. Grasping: GraspNet-1Billion model or Contact-GraspNet predicts 6DoF grasp poses with antipodal constraint check via collision graph. On steel (shiny surfaces, sensor noise) — additional point cloud cleaning: Statistical Outlier Removal + Normal estimation.

Why Reinforcement Learning is effective for precise manipulation

Learning from Demonstration (LfD)

An operator demonstrates the task once by manually guiding the robot arm (kinesthetic teaching) or via VR interface. The algorithm records trajectories, generalizes via Gaussian Mixture Model (GMM) + Gaussian Mixture Regression (GMR) or Imitation Learning (BC, GAIL). Reproduction with adaptation to variations: no need to reprogram for small changes in part position.

Reinforcement Learning for complex manipulations

Tasks where trajectory planning fails: connector insertion (peg-in-hole, tolerance 0.1 mm), screwing without thread stripping, transferring fragile objects. Sim-to-Real: training in Isaac Gym (NVIDIA) or MuJoCo with randomized friction, mass, geometry. Transfer to real robot via domain randomization + small real-world fine-tuning.

On an industrial connector insertion task: SAC (Soft Actor-Critic) achieves 95% success rate after 2M simulation steps + 2 hours of real-world fine-tuning. 2x faster than classical trajectory optimization.

Force/Torque control

Force/torque sensor (ATI Mini45, Robotiq FT300) + ML allows real-time detection of assembly anomalies: if insertion force exceeds expected profile → part incorrectly oriented → stop before damage.

LSTM on time series of signals Fx, Fy, Fz, Tx, Ty, Tz: classification of "normal insertion" / "misalignment" / "wrong part". Anomaly recall: 0.97, latency: 8 ms — fast enough to stop motion before damage.

Mobile Robotics and AMR

SLAM and navigation

AMR (Autonomous Mobile Robot): LiDAR SLAM (Cartographer, RTAB-Map) for mapping + localization. ML component: prediction of dynamic obstacles (people, forklifts) via object detection (YOLOv8 on fisheye cameras) + velocity estimation.

Fleet Management

Fleet of 30 AMRs: task assignment optimization. Multi-agent RL (MAPPO — Multi-Agent PPO) or MILP for dispatching. System throughput with RL vs. rule-based: +14% with the same infrastructure.

Stack and integrations

Comparison of pose estimation methods

How we develop a perception pipeline: step by step

1. Sensor calibration: calibrate RGB-D cameras and force/torque sensors, calculate robot-to-camera transformation matrices.
2. Data collection and annotation: capture 200–500 real frames for the reference object, annotate 6DoF poses.
3. Model training: select architecture (FoundationPose / GDR-Net), run training on synthetic + real data.
4. Testing and fine-tuning: validate on production scene, fine-tune with domain randomization if needed.
5. Integration with robot: connect via ROS 2, configure grasp planning with MoveIt 2, verify perception→grasp→place cycle.
6. Validation on real objects: measure bin picking success rate (target ≥90%), positioning accuracy.

What's included in the work

• Development of perception pipeline (camera calibration, data annotation, model training)
• Integration of motion planning with MoveIt 2 and OMPL
• RL training for precise manipulations (Isaac Gym, MuJoCo)
• Force/torque control with anomaly detection
• Fleet management for AMR fleet
• Pipeline documentation, operator training, 6 months post-implementation support

Development timeline: 4–8 months for perception + grasp planning on a specific part/task. Complete system with RL-trained manipulation and fleet management: 10–18 months. Cost is calculated individually after a production audit. Contact us — we will assess your project and offer a turnkey solution. Request a production audit to find out exact timelines and development cost for your task.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.