Embodied AI founders: tactile data, human-in-the-loop beat waiting for emergence
At a panel at the 2026 (20th) DEMO CHINA event, Wang Yu of Daimon Robotics, Mo Yilin of Lingyu Intelligence and Qin Shentao of OriginFlow discussed how embodied AI moves from research to real settings. Mo argued there is no need to wait for a large-model-style "emergence" moment: his company uses an L2-like human-in-the-loop mode, with robots already doing supermarket sampling, crab-catching and drink-mixing tasks, which he says can hit 70-80% of human efficiency on 70-80% of the work with human backup. Wang said force and tactile data cannot be replaced by video, while Qin collects human motion signals via non-invasive surface EMG and argued that data volume alone is not value — systems engineering and economic efficiency are the real moat.
The discussion sketches a more pragmatic path for embodied AI: put robots into real settings to gather tactile and human-interaction data instead of waiting for a general-purpose robot to emerge.