Shenzhen Embodied AI Company Secures Billion-Yuan Financing from Inovance and China Telecom; ‘Visual-Tactile’ Sensor Shipments Lead the Industry | Hardcore First
A hundred-million-dollar investment has been directed toward a little-known robotics company, betting on "tactile sense"—the most fundamental and yet instinctively captivating of human abilities. Daimon Robotics has secured the funding, but what’s even more intriguing is that it has touched a nerve in the collective anxiety of the embodied intelligence industry: behind all the flashy demos, robots must ultimately make real contact with the sticky, hard, and slippery physical world. The current i
Analysis
A robot built solely on a vision-based approach is like a giant infant with severe visual impairment and tactile dysfunction. It can see an egg but doesn’t know it’s fragile; it can recognize a sponge but doesn’t understand the mechanics of compression and rebound. CEO Duan Jiangya’s metaphor is spot-on: vision tells you where an object is, but touch tells you what happens when you make contact. This lack of "physical common sense" is the biggest chasm preventing laboratory robots from entering everyday homes. Daimon’s bet is essentially reinventing a "baby-like" cognitive approach for robots—building a world model through touch. The idea is both archaic and radical.
Yet the barriers to building a tactile world are far higher than developing a suite of vision algorithms. This isn’t just about adding a sensor. Daimon’s claimed "data flywheel" and "exogenous" data collection network sound promising—taking the lab outside to conduct distributed, crowdsourced data collection to address the authenticity of data. It’s a clever concept because even the most sophisticated lab cannot simulate the complex mechanical interactions of grabbing a live fish at a wet market. However, reality is harsh. Tactile data lacks the relatively unified representation of vision. Aligning over a dozen modalities—pressure, deformation, texture, stiffness, etc.—with vision and motor commands at millisecond-level spatiotemporal precision is an engineering challenge as daunting as embroidering in a storm. The so-called "world’s largest tactile dataset" and "open-source 10,000 hours" are more like planting a flag in a desert to prove water exists—yet an oasis remains far off.
In Daimon’s narrative, "defining standards" is a recurring theme. The release of the Daimon-Infinity dataset and the RobOmni Benchmark makes the intention clear: they don’t want to be just a technology provider but a rule-maker. In a chaotic period without industry standards, whoever first establishes a widely accepted evaluation system secures a pivotal ecosystem position. It’s a smart move but also risky. Standards can’t be established by a single company publishing a few papers; they require rigorous testing across real, diverse, and extreme scenarios and need recognition from both competitors and downstream customers. Otherwise, the "standard" becomes self-talk.
From a capital market perspective, the joint investment from Inovance Technology and China Telecom reflects industry consensus on the "physical interaction" track. Inovance, rooted in industrial automation, understands the value of "tactile sense" for precision manufacturing; China Telecom, betting on cloud-network integration and robotics, sees the explosive potential of "tactile intelligence" in service, inspection, and other scenarios. This funding isn’t just a bet on Daimon alone—it’s a wager on the belief that the "tactile-first" technological path will ultimately prove viable.
Yet between vision and reality always lies a deep chasm called "engineering." Daimon claims its self-developed visuotactile sensors have the world’s first shipment volume—a remarkable milestone, indicating that hardware groundwork has begun to pay off. But from sensors to stable, low-cost, easy-to-deploy modules, and then to enabling robots to reliably perform delicate tasks like "threading grapes" in dynamic home environments, countless pitfalls remain. A robot that can pour water perfectly may require tens of thousands of failures and hundreds of millions of data iterations. The flywheel has started turning, but to generate sufficient commercial momentum, it still needs time, patience, and more real investment.
Ultimately, Daimon’s story is a microcosm of embodied intelligence stepping from sci-fi imagination into reality. When the industry is no longer satisfied with somersaults and dances but starts grappling with how to "pick up a sponge just right," it signals that the true moment of application is near. Daimon has chosen a path that seems laborious but may be the most direct—letting robots re-learn humanity’s most primitive cognitive approach. The journey is long, but the direction may be right. After all, humans evolved over millions of years to learn tool use. Why should we expect robots to master it in a few years with just a few vision models? Touch may be the key to unlocking the real physical world. As for whether Daimon can ultimately "define the standards" and become the forger of this key, let’s calmly observe and give it time.
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