Container and trailer unloading is one of the highest-variability manipulation tasks in logistics. Human loaders pack trailers under time pressure. Boxes arrive at angles from 5° to nearly horizontal. Stack heights vary. Labels face every direction.

TorqueFlow reasons about the 3D spatial layout of each load as it encounters it. It identifies box pose, estimates stability, and sequences picks to maintain structural integrity across the stack. It handles specular tape, partial occlusion, and unlabeled cartons that would cause a vision-only system to pause or fail.

Kitting and custom order assembly require a robot to work through sequences that change with every order. Item configurations vary. Packaging is often deformable. The robot needs to reason about what it's holding, not just where it is.

TorqueFlow handles the contact-rich manipulation that kitting demands such as deformable bags, variable bin layouts, and multi-step assembly sequences across changing order configurations.

Mixed-SKU sorting demands speed and accuracy across items that vary in size, weight, surface finish, and packaging type. Reflective packaging, flexible polybags, and items that shift on the belt aren't edge cases — they're what your line runs on.

TorqueFlow's physics-reasoning handles specular surfaces, soft packaging deformation, and partially occluded items at full line speed, outputting pick-and-place commands in under 30ms without sacrificing accuracy on the hard cases.

QA and exception handling require identifying anomalies, classifying failures, and routing items without slowing production throughput. Packaging, lighting conditions, and product configurations often vary across operations.

TorqueFlow combines visual understanding and physical reasoning to support automated inspection and pick-and-divert workflows. The model identifies dimensional deviations, packaging issues, and handling exceptions while maintaining line-speed operation.