Retail and replenishment
For: retailers and pilot partners exploring stock movement, aisle assistance and replenishment workflows.
OpenAMRobot’s direction combines mobile navigation with a lift and arm-agnostic manipulation layer. Near-term pilots should isolate what the mobile platform can do reliably from experimental manipulation work.
Mobile-first
Transport, mapping, aisle navigation, named locations and task monitoring.
Manipulation pilot
Constrained objects, known poses, explicit workspace and human-supervised evaluation.
Try it: the dark-store pick simulation shows OpenAMRobot 2.0 picking a grocery order from two aisles and handing it over at a packing table, with reach and grasp checks for every shelf location. It is a visualization only; grocery requirements belong to OpenAMRobot 3.0.
Measure: task completion, intervention rate, cycle time, item-handling success, aisle disruption and recovery behavior.
Pilot principle
Begin with one repeatable workflow and a narrow object/environment envelope. Expand only after the acceptance metric is met.