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We are excited to introduce the ten winning sub-projects of the SOPRANO Open Call, each of them being innovative teams pushing the boundaries of Multi-Human Multi-Robot (MH-MR) collaboration. To power their next steps, SOPRANO is investing up to €1.88 million to help SMEs and startups build new or enhanced demonstrators. Each project was able to request up to €200,000, with startups funded at 100% of their budget and SMEs up to 70%. The selected projects will run over 16 months (August 2025 to November 2026), moving from concept to robust demonstrations with measurable impact.

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Explore the awarded sub-projects below!

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1) AUTOLOAD

Proposal name: Autonomous Robot System for Truck Trailer Loading in Logistics Environments

Awarded organisations:

• ECEON Ltd. (Bulgaria): €81,217.41

• ECEON GmbH (Germany): €118,778.75

Total grant (project): €199,996.16.

Project description: AUTOLOAD is developing an autonomous robotic system for truck trailer loading – currently a manual, labor-intensive task with no viable automated solution in Europe. The safety-certified robotic platform enables the automated loading of truck trailers, allowing a single human operator to manage multiple robots simultaneously. The project integrates several SOPRANO components – Non-Visual Localization (NVL), AI Trustworthiness (AIT), AI Model Optimizer (AIO), MH-MR Architecting Tools (MAT), and MLOps Orchestrator (MLO) – ensuring alignment with SOPRANO’s Industry 5.0 vision. The solution emphasizes safe human-robot collaboration, contextual adaptability, and operational efficiency. With confirmed interest from a wide range of industrial customers, the project demonstrates strong commercial potential and will validate SOPRANO technologies in the logistics sector through an integrated industrial demonstrator.

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2) COMPASS

Proposal name: Collaborative Orchestration of Mobile Platforms for Autonomous Social Support
Awarded organisation:

• Caboto Società a Responsabilità Limitata (Italy): €200,000.00

Total grant (project): €200,000.00.

 

Project description: COMPASS addresses urgent healthcare logistics gaps in remote, mountainous regions like Italy's Valle d’Aosta, where aging demographics and geographic isolation challenge medical access. The project proposes an autonomous last-mile drug delivery system using quadruped robots operating from modular logistic hubs retrofitted with Delta robots for automated package sorting. COMPASS will integrate six core SOPRANO components into a ROS 2 architecture. This enables dynamic multi-robot coordination (one real robot validated, others simulated for wider fleet behavior), robust perception and resilient navigation in challenging terrains (alpine paths, stairs), and safe, intuitive human-robot interaction via an onboard, privacy-centric voice interface (Whisper/LLaMA) in French and Italian. The project aims to elevate the system from Technology Readiness Level (TRL) 5 to TRL 7 by validating it across three pilot villages, involving at least 10 community members in user studies, and demonstrating a ≥30% reduction in caregiver delivery workload. Expected impacts include enhanced healthcare accessibility for isolated populations, reduced burden on caregivers, lower carbon emissions from fewer car journeys, and the development of a scalable Robot-as-a-Service (RaaS) model for wider European adoption, significantly benefiting social inclusion and demonstrating SOPRANO's versatility.

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3) DESCANT

Proposal acronym: DESCANT

Proposal name: Smart Dynamic Human-Robot Collaborations for Acoustic Sensor Assembly  using SOPRANO Next-Generation Technologies
Awarded organisation:

• Unparallel Innovation, Lda (Portugal): €139,999.23

Total grant (project): €139,999.23.

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Project description: DESCANT addresses a critical bottleneck in acoustic sensor manufacturing by deploying a collaborative human-robot workstation at Unparallel Innovation's SPL-Meter production facility. The SPL-Meter is a low-cost, Class-2 environmental noise sensor priced under €100 and integrated by clients such as Libelium. While PCB assembly is largely automated, precision manual tasks—microphone soldering and acoustic calibration—create quality and throughput constraints. The project integrates four SOPRANO components (Object Perception, Human Monitoring, Task Allocation, and Advanced Visualizations) to create a modular workstation where an ABB YuMi Cobot assists technicians by automating post-assembly electrical validation, performing inline acoustic calibration and measurement, monitoring thermal and electrical parameters, and providing real-time ergonomic feedback. The SPL-Meter's analogue signal processing architecture ensures privacy by design with no audio recording. Deployed in a real production environment, the solution targets a 37.5% throughput increase, a 66% reduction in soldering defects, and enhanced worker safety and ergonomic comfort. DESCANT demonstrates how Industry 5.0 principles can deliver measurable improvements in high-mix, low-volume electronics manufacturing, offering a scalable, replicable model for European SMEs and supporting Europe's digital and sustainable manufacturing leadership.

 

4) ESSENTIAL

Proposal name: Environmental and Social Sensing for Enhanced Navigation, Tracking, Identification, and Adaptive Locomotion
Awarded organisations:

• Essential Robotics ApS (Denmark): €200,000.00

Total grant (project): €200,000.00
 

Project description: ESSENTIAL proposes to automate the transportation of empty hospital beds to and from centralized washing stations using an autonomous mobile robot platform. This task is currently manual, physically demanding, and error-prone - costing hospitals time, labor, and hygiene compliance. Our compact, low-profile robot is uniquely capable of lifting and moving hospital beds in complex, human-populated environments such as corridors and elevators. Through the SOPRANO program, we aim to advance our existing TRL 5 prototype to TRL 7 by integrating four SOPRANO technologies: Visual Localization and Mapping (SC01), Object-Based Perception (SC02), Human Monitoring (SC04), and the AI Model Optimizer (SC15). These modules will enhance the robot’s situational awareness, enable reliable marker-free docking, ensure safe human-robot interaction, and support real-time performance on constrained hardware. A fifth module—Responsible AI & Ethics (SC10)—will be used to ensure transparency and trustworthiness. The project will validate multi-human, multi-robot (MH-MR) coordination through a dual-robot deployment in a real hospital setting. Expected outcomes include a fully integrated demonstrator with validated safety, robustness, and efficiency under real-world constraints. The project directly supports SOPRANO's goals of enabling scalable, human-centric robotics in agile industrial environments—contributing a novel use case in healthcare logistics.

 

5) FAROS

Proposal name: Federated AI-Supported Collaborative Robotic Welding for On-Site Steel Construction
Awarded organisation:

• 3D Components AS (Norway): €140,000.00

Total grant (project): €140,000.00.

Project description: FAROS introduces a federated AI-powered collaborative robotic welding system for on-site steel construction, addressing challenges such as excessive manual welding costs with limited productivity and inconsistent quality. Through SOPRANO's Multi-Human Multi-Robot components and 3D-Components' RobTrack, FAROS enables simultaneous operations reducing costs while boosting productivity. With FAROS, the RobTrack system will become the first federated platform capable of optimising welding parameters in real-time while serving them across multiple operations. This enables human-robot coordination in dynamic environments, parameter optimisation across diverse weld types, and quality assurance with digital traceability. Ultimately, FAROS will create a "welding-as-a-service" model, further reducing capital investment barriers. In 16 months, FAROS will deliver a TRL7 prototype with at least 40% higher productivity than standalone systems, 95% accuracy (R²) in AI-driven optimization, at least 50% cost reduction per weld meter, and a viable performance-based business model. This will support European construction automation, strengthening competitiveness through sustainable building methodologies.

 

6) FRACTAL

Proposal name: Flexible Robotic Assistants for Ergonomic and Scalable Operations in Personalized Plastic Manufacturing

Awarded organisations:

• RobotiCo PC (Greece): €111,875.00

• Bendabl PC (Greece): €48,500.00

• Castalia Technologies SMPC (Greece):  €27,650.00

Total grant (project): €188,025.00.

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Project description: FRACTAL addresses the growing need for automation in small-batch, high-mix personalised plastic produc-tion environments. Targeting the manufacture of laparoscopic training kits by the BoxLap product line, FRACTAL integrates collaborative robotics into workflows that are currently labor-intensive, ergonomically hazardous, and highly variable due to frequent customisation. The project proposes the deployment of two robotic platforms: a mobile cobot to manage material transport and handoffs across the shopfloor, and a portable, workstation-based cobot to support demanding tasks such as mould assembly and spraying. These systems will be enhanced with advanced SOPRANO technologies, including Object Perception (OBP), Task Allocation (CTA), Robotic Capabilities Implementation (RCI), Simulation-Based Testing (SBT), and Human Digital Twin (HDT)—to enable safe, adaptive, and human-aware collaboration. By offloading repetitive and unsafe operations to intelligent robotic assistants, FRACTAL will reduce cycle time by 20%, lower ergonomic risks by over 40%, and enhance production scalability without compromising product personalization. The project will demonstrate four real-world collaborative scenarios and achieve Technology Readiness Level 7 within the funding period. FRACTAL contributes to the digital transformation of European SMEs by offering a reconfigurable automation solution that supports inclusivity, reduces environmental waste, and alleviates labor shortages.

 

7) HR_PickAIR

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Proposal name: Human-Robot Collaboration for Smart, Safe and Efficient Industrial Waste Sorting

Awarded organisation:

• ROBENSO I.K.E. (Greece): €176,875.00

Total grant (project): €176,875.00.

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Project description: HR-PickAIR addresses key challenges in waste recycling by introducing a novel human-robot collaboration system for material sorting. While robotic sorters powered by Deep Learning (DL) have modernized Material Recovery Facilities (MRFs), they still struggle with accurate object classification, impacting material purity and market value. As a result, human involvement remains essential—but comes with health risks due to hazardous working conditions. Embracing Industry 5.0 principles, HR-PickAIR aims to protect human workers by relocating them to clean control environments, where they assist sorting via touchscreen interfaces while robots handle physical waste picking using autonomous suction-based gripping. The system includes a smart interface for monitoring and configuring the sorting process, and a dynamic task allocation strategy to optimize collaboration between multiple human operators and robotic units. The project will leverage five SOPRANO technologies—AIT, AIO, AVT, CTA, and MRC—and validate its approach in a real-world waste sorting setup to demonstrate market readiness.

 

8) REWARD

Proposal name: Robotics-Enhanced WEEE Adaptive Recycling and Disassembly

Awarded organisation:

• HIRO Robotics S.R.L. (Italy):  €140,000.00

Total grant (project): €140,000.00.

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Project description: REWARD leverages SOPRANO technologies to develop a collaborative Multi-Human Multi-Robot System for efficient disassembly of Waste Electrical and Electronic Equipment (WEEE), focusing on flat panel displays (FPDs), a fast-growing, complex e-waste stream. Current disassembly methods either rely on manual labour, risking ergonomic injuries and inconsistent quality, or expensive, rigid automation. Human-Robot Collaboration (HRC) offers a balanced approach: robots handle repetitive, injury-prone tasks (e.g., unscrewing), while humans manage complex operations (e.g., cable removal, sorting), supported by AI to reduce errors and improve efficiency. However, today’s HRC systems face synchronisation challenges, leading to bottlenecks and reduced scalability. REWARD addresses these issues by integrating SOPRANO's Human Monitoring (HMO) and AI Trustworthiness (AIT) tools within HIRO Robotics' TEIA work cell. This setup enables real-time adaptation to human actions, ensuring safety, responsiveness, and improved throughput. Key goals include handling high WEEE variability and ensuring robust data fusion for smooth collaboration. REWARD aims to demonstrate a 15–20% throughput gain, 5–10% improvement in sorting accuracy, and enhanced ergonomics and inclusivity for workers of varying abilities and experience.

 

9) YardMate

Proposal name: YardMate: Multi-Robot Collaborative Shunting System for Human-Centric Yard Logistics

Awarded organisation:

• ANT Machinen GmbH (Germany):  €200,000.00

Total grant (project): €200,000.00.

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Project description: YardMate is a SOPRANO use-case project that will demonstrate how mobile robots can work safely and efficiently alongside human operators in outdoor logistics environments. The project is led by ANT Machines, a robotics company based in Dortmund, Germany, that develops electric driverless robots designed to move heavy trailers and containers within logistics yards — an area traditionally handled by diesel-powered trucks and manual drivers. The project will conduct a real-world pilot at a logistics test site of Schenker AG in Dortmund to validate the collaborative operation of multiple robots under realistic working conditions, including interaction with human personnel. The pilot will deploy two ANT Yard Robots to carry out trailer movements at an operational logistics site. These robots will be enhanced through the integration of SOPRANO components to support real Multi-Human Multi-Robot (MH-MR) collaboration, enabling robots and human operators to coordinate tasks safe-ly and efficiently. Compared to current diesel-powered, manually operated shunting processes, the YardMate solution aims to reduce task cycle times by 10–20%, lower unnecessary robot movement by at least 20% through optimized task sequencing, and eliminate up to 150 tonnes of CO₂ emissions annually per diesel vehicle replaced.

 

10) YOUNIC

Proposal acronym: YOUNIC

Proposal name: Your cOllaborative hUman-robot iNteraction for Innovative fashIon logistiCs
Awarded organisations:

J&W S.R.L. (Italy): €140,000.00

Total grant (project): €140,000.00

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Project description: The YOUNIC project aims to create a groundbreaking logistics prototype specifically for the fashion sector, designed to boost efficiency, safety, and sustainability. Imagine a system where instead of operators walking aisles, items needed for orders are brought to them via an automated line using AMRs and cobots. Once orders are ready, they're moved to shipping or storage using autonomous vehicles. A key principle is truly centering the human operator in this process, supporting them with technologies like AI and wearables to help with tasks and decisions, ultimately enhancing safety and reducing stress, very much aligned with Industry 5.0 ideas. This prototype, being developed in J&W's Human-Centric Manufacturing Lab, pulls together various technologies including cobots, AMRs, AI, and vision systems. J&W is using their own 'willie' platform for advanced human-robot interaction and ROS 2 to coordinate everything. They're also integrating specific SOPRANO components, like Human Monitoring and Human Digital Twin, to better understand and support the operator. Starting from a validated TRL 5, YOUNIC plans to reach TRL 7. The big payoff is expected to be significantly improved efficiency (30-40%), safety (40-50%), and flexibility (50-60%), alongside higher levels of human-machine interaction (70-80%). Plus, the solution is built to be easily scalable and adaptable for other industries too.

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