外滩大会三问具身智能： 模型、数据、生态如何突围？

TL;DR - At the 2026 Inclusion Bund Conference, researchers and robotics executives argued that embodied AI must move beyond impressive demos toward robots that operate safely, continuously, and economically across real-world settings. Progress depends on jointly improving physical-world models, targeted data feedback loops, and an ecosystem capable of replicating deployments at low cost.

• General-purpose robot models must continuously fuse vision, touch, and other sensor streams into executable actions, rather than simply reuse architectures designed for digital-world multimodal models.
• Model progress should be measured by reduced deployment costs and reliable long-duration operation in changing environments, not isolated task success or benchmark rankings.
• Data advantage increasingly comes from identifying specific model weaknesses, collecting targeted examples, and feeding real-world failures back into training—not merely accumulating larger datasets.
• Ant Lingbo demonstrated one general-purpose “brain” across pharmacy, logistics, and industrial robots and launched a LingBot-VLA 2.0 challenge, while emphasizing that scalable commercialization requires collaboration among model, hardware, data, chip, and deployment partners.