Verified Events
DateTypeCompanyHeadlineSegment
Mitsubishi Electric develops humanoid–CNC machining coordination system
- The architecture combines an AI machining task planner, a CNC tool with physical AI and a general-purpose humanoid.
- Target tasks include setup, cleaning, finished-part removal and transport, inspection and troubleshooting.
- The company plans to demonstrate the system at JIMTOF in Tokyo from October 26, 2026.
GenON validates Nike outsole AI inspection and wins follow-on work
- The company reports defect-detection rates of 99.7% and 99.2% for two footwear styles.
- New work includes upper inspection and outsole pressing with collaborative robots, vision and motions learned from operator teleoperation demonstrations.
- Production-model development is planned before year-end, with factory-line introduction from 2027.
Mitsubishi MELFA gains HoloLab arm-pilot XR teleoperation software
- The supported XR device is Meta Quest 3.
- Intended uses include hazardous environments, variable tasks and capturing operator demonstrations for Physical AI.
- HoloLab is recruiting manufacturing and logistics partners for proofs of concept.
Siemens opens six embodied-AI validation scenarios at five Chinese factories
- Scenarios include bulk-part sorting and loading, container transport, depalletizing and palletizing.
- Other tasks cover process operations and quality inspection, precision screw fastening and finished-product packaging.
- The programme aims to test transfer across production lines and operating scenarios.
Neuromeka subsidiary Robolution joins autonomous manufacturing R&D programme
- The July 2026–December 2029 programme totals KRW10.4 billion, including KRW7.38 billion in government R&D support across the project.
- Robolution will develop autonomous material transport and torque-wrench assembly with vision and force-based correction; factory validation is planned.
- Programme targets include 20% higher assembly productivity and an 80% intelligent-process automation rate.
Chef Robotics adopts NVIDIA simulation and motion planning for food assembly
- Digital twins test robotic cells against variations in ingredients, containers, utensils and production-line layouts before installation.
- GPU-accelerated motion planning supports piece picking, multi-deposit assembly and coordinated robot lines in cluttered environments.
- Chef says customer deployments achieve up to 60% higher labour productivity.