What would it cost to implement this technology in our warehouse?+
The project does not publish per-unit licensing or integration costs. The technology was demonstrated on commercially available robots (KUKA, Franka Emika Panda, UR), which means implementation would involve software integration on top of existing robot hardware rather than buying entirely new systems. Contact the consortium for pricing discussions.
Can this scale to a full warehouse operation with hundreds of robots?+
The technology was validated in three realistic logistics scenarios (tossing, boxing, grabbing) on individual robot stations. Scaling to a full warehouse would require integration with existing warehouse management systems and multi-robot coordination, which was not the focus of this project. The underlying algorithms are designed for real-time operation, which is a prerequisite for scalability.
Who owns the IP and can we license this technology?+
The project was funded under Horizon 2020 as an RIA with EUR 4,357,985 in EU contribution across 9 partners. IP is typically shared among consortium members according to their grant agreement. Licensing discussions would need to go through the coordinator, Technische Universiteit Eindhoven, and relevant industrial partners.
How much faster are these robots compared to what we use now?+
The project objective states 10% shorter cycle time for applications requiring dynamic manipulation in logistics. This was benchmarked across three scenarios comparing dynamic (non-zero contact speed, impact-aware) operation against standard (near-zero speed, impact-unaware) methods. The 10% figure applies to the manipulation phase specifically.
Does this work with the robots we already have?+
The technology was tested on three commercially available robot platforms: KUKA dual-arm systems, Franka Emika Panda, and Universal Robots (UR). If your facility uses any of these platforms, integration is more straightforward. The approach leverages torque-controlled robots, so compatibility depends on your robots having force/torque sensing capabilities.
What happens when the robot hits something it shouldn't?+
The project developed dedicated impact-aware sensing (I.Sense) that distinguishes between expected and unexpected contact events in real time. This means the robot can detect when something goes wrong during a high-speed grab or toss and react appropriately, rather than blindly continuing. This is a key safety advantage over simply programming robots to move faster.
Is this proven in a real logistics environment or just a lab?+
Based on available project data, the three scenarios (toss, box, grab) were tested on real robot hardware with benchmarking results documented in technical reports. The scenarios simulate realistic logistics tasks — bin-to-belt tossing, bin-to-bin boxing, and case depalletizing. However, the deliverables describe these as controlled test environments rather than live warehouse deployments.
