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.