Production Robot Scenario Selection

Production robot scenario selection is Gao Jiyang’s method for deciding where Xinghaitu should commercialize Embodied AI. The source frames good early scenes as those where current robot capability can create real value without requiring extreme speed, zero-error reliability, or narrow one-off customization.

170: 【具身季报 26Q2】世界模型大风不停，和不想被贴标签的人 adds Robot Logistics Sorting as a concrete humanoid-robot wedge. Logistics sorting is bounded enough to show customer value, but still exposes tail cases such as soft packages, odd shapes, fallen objects, and labels that need flipping or flattening.

Key Claims

• Gao defines the supply side through speed, precision, and generalization, with current attention on near-human speed, centimeter-level manipulation, and few-shot or zero-shot adaptation.
• Good early scenes should not demand very high speed, should tolerate limited failure cost, and should have global scaling potential.
• The source groups labor actions as Carry, Pick, Pack, Fold, and Operate.
• The scenes Gao names favor warehouse logistics, bin picking, and in-factory logistics over broad household generalization.
• Scenario choice is connected to data: the right scene should create useful, repeated, grounded data for model improvement.
• The LateTalk source reinforces logistics as a realistic early proving ground because it combines repeatable labor, messy manipulation, and clearer buyer understanding than entertainment-style robot demos.

Connections

• Xinghaitu and Gao Jiyang — company and source speaker.
• Wheel-Based Dual-Arm Robots — robot form chosen for the target work.
• Physical World Data Flywheel and Real Robot Data Strategy — data loop that depends on scene access.
• Product Led Willingness To Pay and Customer Pull — demand signals the production scene must eventually prove.
• Robot Logistics Sorting, Figure AI, Xingdong Era, and Dexterous Manipulation — Q2 2026 logistics-sorting examples and manipulation constraints.