This workshop addresses the gap between cutting-edge computer vision research and its practical application in industrial robotics, specifically addressing challenges in tasks like reliable, scalable, and cost-effective bin picking. The workshop brings together researchers and practitioners to discuss topics including 3D scene understanding, embodied AI, and robot learning, focusing on developing robust solutions by considering factors like embodiment, camera choice, and data needs. Complementing the workshop, the Perception Challenge for Bin Picking offers a practical platform for participants to tackle real-world 6DoF pose estimation problems using a robot-in-the-loop evaluation, providing a more realistic performance assessment than traditional vision-only metrics. The workshop and challenge together aim to accelerate the adoption of vision-guided robotics and enhance industrial automation efficiency.