独家｜订单翻了10倍 这家触觉感知公司又融资了

This article centers on the new round of Pre-A+ financing completed by Yaole Technology, a Chinese haptic perception company, with key takeaways as follows:

1. Yaole Technology is not merely a flexible tactile sensor manufacturer. Its long-term positioning is to build a haptic data platform that captures real interaction data between humans and the physical world, converts it into trainable standardized data assets, and feeds this data into the training pipeline of AI world models to fill the huge current gap in physical interaction data.

2. Its core go-to-market strategy is to start with automotive-grade fabric tactile sensors, refine its technology to meet the automotive industry’s highest standards, then expand into lower-threshold scenarios such as embodied intelligence and elderly care. It has already achieved mass production and delivery of smart seat solutions for first-tier luxury carmakers, secured design wins from multiple leading automotive OEMs, and expects its order volume to increase 10-fold by 2026, with nearly half of the incremental growth coming from embodied intelligence.

3. Its core advantage lies in a team with full-stack capabilities covering technology, algorithms, mass production and commercialization. By choosing fabric as the sensing medium, it meets the demand for unobtrusive real-world data collection, and will launch a smart fabric glove capable of data collection this August.

This article offers high-value insights into the development trend, product R&D logic and commercialization strategy of the haptics sector for brand operators in AI and smart hardware, with key takeaways as follows:

1. In terms of consumer and industrial trends, as AI moves toward real-world deployment, there is a global gap of over 95% for scalable real physical interaction data. Haptic technology has shifted from an optional technology to a must-have. Smart cabins, elderly care, embodied intelligent robots and consumer wearables are all new blue oceans of growth, and market demand has already been validated through commercial deployment.

2. For product R&D, Yaole’s logic of choosing fabric as a sensing medium is highly instructive: to collect real human behavior data, providers must not alter users’ existing habits. Using fabric, a material users interact with daily, as a carrier fits user needs better than rigid, bulky sensors, and is also much easier to scale.

3. For commercialization strategy, players can learn from Yaole’s approach: enter a high-standard industry first to complete mass production validation, build technological credibility, then expand to lower-threshold scenarios. This path makes it easier to win recognition from capital and major clients, and build industry barriers rapidly.

This article clarifies the opportunities, risks and actionable lessons for sellers in the haptics and AI sensor space, with key takeaways as follows:

1. For market opportunities, there is currently a global gap of over 95% for physical interaction data usable for AI training. Haptics has become a core necessity for AI’s real-world deployment, with smart cabins, embodied intelligence, robots and elderly care all being high-growth segments. Yaole Technology expects its order volume to increase 10-fold by 2026, with nearly half of incremental growth coming from embodied intelligence, indicating strong growth certainty for the sector.

2. For risk warning: Most current players in the sector still remain at the stage of selling sensors and delivering hand-built samples. They have not achieved mass production, nor do they have the capability to provide standardized data. Single-material technology can hardly build sustainable barriers, making it hard for these players to win in competition.

3. For actionable lessons: Enter high-standard sectors such as automotive first to complete mass production validation and refine technical capabilities, then expand into other lower-threshold scenarios. This approach makes it much easier to build trust and barriers, and secure support from capital and industrial resources.

This article clarifies product requirements, business opportunities and digital transformation insights for manufacturing factories in the haptics sector, with key takeaways as follows:

1. For product design and manufacturing requirements: The current market demands thin, flexible, fatigue-resistant flexible sensing products that can collect real interaction data unobtrusively. Automotive-grade products have extremely high requirements and must pass hundreds of tests covering extreme temperatures, vibration, and electromagnetic interference. Dense sensing placement in limited space, millisecond-level response, and high consistency are core technical requirements.

2. For business opportunities: Fabric-based sensing is a new direction in the sector. Integrating sensors into products users interact with daily such as gloves, seats, mattresses and insoles, its market demand has been validated across multiple scenarios, with stable orders from elderly care, smart cabins and embodied intelligence. Leveraging the economies of scale of textile manufacturing can bring costs down to the level of consumables, opening a huge market space.

3. For transformation insights: Building a full in-house production line from yarn to sensing network, and controlling process quality across all links, is the only way to guarantee product stability. Completing mass production validation for clients in high-requirement scenarios such as automotive first, then expanding to other scenarios, makes it much easier to secure long-term orders.

This article clarifies sector trends, client pain points and viable paths for service providers serving AI, robotics and automotive electronics, with key takeaways as follows:

1. For industry development trends: After the hype around embodied intelligence cools down by 2026, physical interaction data will become the core necessity for AI’s real-world deployment. Haptics is the core scarce modality to fill the data gap, with a very high market ceiling. Players that achieve mass production, multi-scenario coverage and standardized data frameworks early will have the opportunity to define the training paradigm for the next generation of physical AI.

2. For current core client pain points: Industry clients’ demand has shifted from "whether haptic hardware is available" to "whether train-ready, directly usable standardized data is available". Most current players can only provide hardware, and cannot meet the demand for large-scale data collection. Even virtual data generated by simulation cannot close the reality gap between virtual and physical worlds.

3. For solution directions: Using fabric as the sensing carrier, leveraging large-scale textile manufacturing to cut costs, refining mass production capabilities to meet automotive-grade standards first, then supporting multiple lower-threshold scenarios, and gradually building a standardized data annotation and supply system to form a closed loop of hardware scaling + data supply is a viable development path at this stage.

This article clarifies project screening criteria, investment layout directions and risk mitigation points for hard tech investors and industrial service platforms, with key takeaways as follows:

1. For project screening criteria: Screening criteria for the haptics sector are now clear, and no longer focus on paper specifications and impressive demos. The core evaluation is based on three dimensions: first, whether the project has secured design wins or even mass production delivery in automotive, an industry with complete validation systems and mature supply chain barriers; second, whether it can achieve mass production at scale and control production costs; third, whether it can accumulate standardized, usable physical interaction data on top of its hardware business.

2. For layout and sourcing directions: Priority should be given to projects with full-stack integrated capabilities across materials, processes, mass production, calibration and algorithms, that have secured design wins and mass production with leading automakers, have backing from top-tier industrial capital, and position themselves as long-term haptic data platform players. These projects have higher barriers and greater growth potential.

3. For risk mitigation: The sector’s technology trajectories have not yet converged, with relatively high uncertainty. Investors should avoid projects that only deliver samples, lack mass production capabilities, and focus solely on hardware without data accumulation. These players can hardly build long-term barriers and are very likely to be eliminated by the market.

This article reveals the latest industry developments, unresolved challenges and innovative business models in the haptics sector for AI and robotics industry researchers, with key takeaways as follows:

1. For new industry developments: As AI moves toward real-world deployment, there is a global gap of over 95% for usable physical interaction data for model training, and haptics has become the core scarce modality to fill this gap. Mature automotive-grade haptic technology expanding downstream into embodied intelligence has emerged as a new industry direction, attracting sequential investment from leading industrial and financial investors including Xiaomi, Ecovacs, and Dinghe Gaoda. Yaole Technology expects its 2026 order volume to increase 10-fold, with nearly half of incremental growth coming from embodied intelligence, validating the sector’s strong growth potential.

2. For new industry challenges: The sector is still in an early stage with no converged technology trajectory. Most players still remain in the hardware-selling stage, with extremely low data standardization, and cannot meet the demand of world models for large-scale reusable data. Single-material technology cannot build high sustainable barriers.

3. For innovative business models: The "hardware scaling + data flywheel" model explored by Yaole Technology uses low-cost fabric sensing hardware as a consumable-style data collection terminal, scales deployment to collect data, then supplies standardized data for AI training. This model generates cash flow from hardware, and builds long-term value from data, providing a referenceable new development paradigm for the entire sector.