Robot Bin Picking Vision Guidance Patents 2026
Robot Bin Picking Vision Guidance Patents 2026
From 3D depth sensing to zero-shot generalization, robot bin picking is accelerating sharply since 2019. This dataset spans 17 patents and 15+ literature records across US, CN, WO, EP, CA, and JP jurisdictions.
Vision-Guided Bin Picking: From Sensing to Grasp Execution
Robot bin picking—automating the identification, localization, and grasping of unordered parts from containers—spans the full pipeline from 3D depth sensing and object detection to 6DoF pose estimation, coordinate-frame calibration, grasp-point selection, and motion-path planning. The field has accelerated sharply since 2019, driven by labor shortages, Industry 4.0 mandates, and maturing 3D sensing hardware.
Core sub-domains identified in this dataset include 3D depth vision and structured-light sensing, deep-learning-based object detection using YOLO variants and CNNs, 6DoF pose estimation from RGB-D and point-cloud data, hand-eye and camera-robot coordinate calibration, adaptive region-of-interest (ROI) selection, multi-modal sensor fusion, simulation and synthetic data generation, and human-robot collaborative picking.
The innovation timeline spans from a foundational 1981 University of Rhode Island patent through Braintech’s commercial-grade hand-eye calibration filings in 2003–2005, a major development cluster in 2012–2019 anchored by Magna International and Cognex, and a frontier phase in 2024–2026 featuring ABB Schweiz, Oxipital AI, and a Chinese closed-loop grasping module, all pointing toward zero-shot generalization.
In this dataset, Magna International leads by filing volume with 6 records spanning US, CA, WO, and EP jurisdictions, followed by Siemens Aktiengesellschaft with 3 records and Braintech/RoboticVisionTech with 3 records. Chinese assignees are active but largely file domestically, while US-based entities show stronger international (WO) protection strategies in retrieved records.
Filing Patterns by Jurisdiction and Technology Cluster
The retrieved patent records reveal distinct geographic concentration and technology cluster distribution. US jurisdiction accounts for approximately 40% of records in this dataset, with China and WO filings representing the next largest shares.
Patent Records by Jurisdiction — Dataset Snapshot
US jurisdiction accounts for the largest share (~40%) of patent records in this dataset, followed by CN (~25%) and WO (~15%), with CA, EP, and JP making up the remainder.
↗ Click bars to explorePatent Filings by Era and Technology Cluster — Dataset Snapshot
In this dataset, the acceleration phase (2020–2023) and frontier period (2024–2026) together account for the majority of deep-learning and multi-modal sensor fusion filings, while foundational calibration and dual-vision patents dominate earlier eras.
↗ Click bars to exploreKey Application Areas for Robot Bin Picking Vision Technology
Patent and literature records in this dataset span four primary application domains: discrete parts manufacturing, warehouse logistics and e-commerce fulfillment, agricultural harvesting, and defense and special environments. Each domain presents distinct sensing and grasping challenges addressed by named assignees.
Industrial Manufacturing and Assembly
Magna International’s patent family (6 records: WO, CA, US×2, EP) explicitly targets discrete parts manufacturing bin-to-machine and machine-to-machine loading. Siemens’s adaptive ROI and bin pose detection systems (3 US/EP records, 2024) target general industrial robotic cells. A 2019 literature benchmark uses the Amazon Robotics Challenge 2017 as a key industrial proxy for bin picking performance evaluation.
Industrial ManufacturingWarehouse Logistics and E-Commerce
Embodied Intelligence’s WO 2021 patent addresses unstructured bin picking modeled on the Amazon Robotics Challenge, including bin perturbation strategies when no high-probability grasp exists. Oxipital AI’s 2026 US and WO filings introduce real-time vision updates triggered when the robotic arm exits the sensor field of view, enabling continuous high-throughput conveyor picking. FANUC America’s augmented reality visualization system (WO, 2020) targets conveyor-belt random-orientation picking with real-time parameter tuning.
Warehouse LogisticsAgricultural Harvesting Robots
Literature documents a watermelon harvesting robot using YOLOv5s-CBAM that achieved a 93.3% success rate with 8.7 mm positioning error (2022). The State of Israel’s Ministry of Agriculture filed a laser-vision integrated human-robot guiding system for agricultural object detection in unstructured environments (WO 2021, US 2022), targeting detection of agricultural objects in noisy and unstructured field conditions.
Agricultural RoboticsDefense, Space, and Special Environments
MIT’s RF-visual grasping patent (US, 2025) uses RFID-tagged items with geometric RF-visual fusion and deep reinforcement learning, explicitly targeting robotic retrieval in warehouses and disaster scenarios involving fully occluded objects. Space applications are addressed in literature (SpaceDrones 2.0, 2022) using synthetically trained domain-randomized computer vision models. Guangxi Power Grid’s binocular-LiDAR patent (CN, 2021) targets high-accuracy target localization for live-line electrical maintenance robotics.
Special EnvironmentsKey Patent Assignees in Robot Bin Picking Vision — Dataset Snapshot
In this dataset, Magna International Inc. accounts for the highest filing volume with 6 records spanning US, CA, WO, and EP jurisdictions, representing concentrated international protection of dual-vision adaptive bin picking. Siemens Aktiengesellschaft holds 3 records in retrieved records, focused tightly on depth-camera ROI selection and automatic bin pose detection filed in 2024.
Top Assignees by Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreMagna International Inc.
Magna International holds 6 records in this dataset spanning US, CA (×2), WO, and EP jurisdictions, with filings dating from 2018 to 2024. Their patent family covers a dual-vision architecture where a first system identifies parts and pick location within a bin and a second system locates the dynamic destination, with integrated quality inspection. The core US filing carries active legal status, creating a potential blocking position for two-vision-system manufacturing bin picking designs.
Canada — CASiemens Aktiengesellschaft
Siemens holds 3 records in this dataset filed in US and EP jurisdictions in 2024, covering adaptive region-of-interest selection for vision-guided robotic bin picking and automatic bin pose detection from depth images. Their adaptive ROI patent crops depth camera input based on bin geometry to identify optimal grasp points, while the bin detection patent enables robotic cell setup without manual configuration. Both filings represent a tightly focused cluster in depth-camera-based industrial bin picking automation.
Germany — DEFive Frontier Vectors in Bin Picking Vision (2024–2026)
The most recent filings in this dataset reveal five distinct vectors of advancement in robot bin picking, spanning zero-shot generalization, gripper-vision coordination on dynamic conveyors, RF-visual fusion for occluded grasping, closed-loop predictive calibration, and LLM-integrated scene graph navigation.
Zero-Shot Generalization for Unseen Objects
ABB Schweiz’s 2025 WO filing uses a generalized segmentation model with confidence-scored candidates and multi-view triangulation to handle objects never seen during training. This directly addresses the setup-cost problem of traditional model-based bin picking, which requires known-object CAD models. The filing signals that major robotics OEMs are now entering the deep-learning generalization space where model-free grasping is currently underprotected in this dataset.
Gripper-Vision Coordination on Moving Conveyors
Oxipital AI’s 2026 US and WO filings introduce real-time vision updates triggered specifically when the robotic arm exits the sensor field of view, enabling continuous high-throughput picking on dynamic conveyors. This approach resolves the sensor occlusion problem created by the robot arm itself during picking motions. The dual-jurisdiction filing strategy (US and WO) suggests active international IP protection for this conveyor-picking coordination method.
Dual-Vision Adaptive Bin Picking vs. Deep-Learning 6DoF Pose Estimation
Click any row to explore further.
| Dimension | Dual-Vision Adaptive Bin Picking (Magna International) | Deep-Learning 6DoF Pose Estimation (ABB Schweiz / Siemens) |
|---|---|---|
| Core Mechanism | First vision system identifies parts and pick location; second independent system locates dynamic destination; controller plans optimal robot path | Generalized segmentation with confidence scoring and multi-view triangulation refinement for 6DoF pose; adaptive ROI based on bin geometry depth camera crops |
| Key Assignees | Magna International Inc. (6 records: US, CA×2, WO, EP, 2018–2024) | ABB Schweiz AG (WO, 2025); Siemens Aktiengesellschaft (US×2, EP, 2024) |
| Object Scope | Known manufacturing parts with defined pick and place locations; includes quality inspection capability | Handles previously unseen objects (ABB); known bin geometry required for ROI (Siemens) |
| Sensing Modality | Dual independent vision systems; optional quality inspection by either system | RGB-D cameras, point cloud processing, depth camera for ROI cropping, multi-view triangulation |
| Training Data Requirement | Does not explicitly require deep learning training; rule-based path planning and vision-location logic | Generalized segmentation model (ABB); deep CNN-based detection and pose recovery requiring training data |
| Application Domain | Discrete parts manufacturing; bin-to-machine and machine-to-machine loading; basket loading | General industrial robotic cells; random bin picking for unseen or varied objects |
| IP Status (Dataset) | Active legal status on core US filing; multi-jurisdictional portfolio creates potential blocking position | ABB WO 2025 frontier filing; Siemens US/EP 2024 active filings — both in this dataset |
Frequently Asked Questions: Robot Bin Picking Vision Guidance Patents
This dataset includes 17 distinct patents and more than 15 literature records retrieved via targeted searches, spanning publication dates from 1981 to 2026 and jurisdictions including the US, CN, CA, EP, WO, and JP.
Magna International Inc. holds the highest filing volume in this dataset with 6 records spanning US, CA (×2), WO, and EP jurisdictions, covering a dual-vision adaptive bin picking architecture for manufacturing applications with filings from 2018 to 2024.
ABB Schweiz AG filed a WO patent in 2025 titled ‘Vision system for random bin picking applications of unseen objects.’ It uses a generalized segmentation model with confidence-scored candidates and multi-view triangulation to refine 6DoF pose for objects that were never seen during training, addressing the setup-cost problem of traditional CAD-model-based bin picking.
MIT’s 2025 US patent employs RFID-tagged items combined with geometric RF-visual fusion and deep reinforcement learning to locate and grasp objects that are fully hidden from cameras. Camera-only systems cannot address full occlusion, and this approach targets robotic retrieval in warehouses and disaster scenarios.
Across all patent records in this dataset, the US accounts for approximately 40% of records, CN approximately 25%, WO approximately 15%, CA approximately 10%, EP approximately 7%, and JP approximately 3%.
The content identifies three main white spaces: (1) model-free or few-shot pose estimation, since most dataset patents depend on known-object CAD models; (2) multimodal sensor fusion beyond RGB-D combining depth cameras with RF, thermal, or structured-light sensors; and (3) closed-loop learning and dynamic recalibration, as predictive error compensation and synchronized multi-coordinate calibration are only beginning to appear in filings.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.