Tashi Zhihang Releases World's First Scalable Real-World Embodied Multimodal Dataset WIYH | BlueRun Ventures Family Headlines

The embodied intelligence industry is on the verge of a data revolution. On October 10, Tashi Zhihang unveiled World In Your Hands (WIYH), the world's first large-scale real-world embodied VLTA (Vision-Language-Tactile-Action) multimodal dataset, with plans to open it to the industry in December 2025. This milestone marks the formal establishment of Tashi Zhihang's industry-first Human-Centric embodied data engine paradigm — a technical approach that is roughly six months ahead of Tesla's Optimus. BlueRun Ventures led Tashi Zhihang's angel round.

For some time, the internet data and simulation data that mainstream large models rely on for pre-training have suffered from two shortcomings: internet data varies wildly in quality and lacks action information; simulation data has limited realism and poor scene generalization, making it difficult to smoothly transfer trained models to the real world. For humanoid robots, the biggest hurdle on the path to "embodied intelligence" isn't the algorithm itself — it's how to obtain scalable, authentic, generalizable data. The absence of high-quality training data has become an industry-recognized bottleneck.

Wenchao Ding, Chief Scientist at Tashi Zhihang, said that the release of the Tashi WIYH dataset marks the industry's first large-scale, cross-industry, cross-task collection of vision, language, tactile, and action multimodal data in the real world, laying the groundwork for future embodied foundation models to achieve scaling laws.

In the Human-Centric first-person data collection videos released by Tashi Zhihang, unlike the static, monotonous environments of previous labs and data factories, Tashi WIYH draws on multiple real-world industry workplaces and workers, collecting data on standard human operating procedures across embodied scenarios including hotel laundry, supermarket stocking, and logistics operations. In short, Tashi WIYH doesn't just solve the problems of "insufficient data, low quality, and high costs" — it ensures the data "comes from the real world."

A laundry worker wearing a self-developed collection device folds towels

Tashi WIYH has four defining characteristics:

• Authentic: Collected from real embodied tasks, closely aligned with actual model application scenarios;
• Rich: Spanning multiple industries and manipulation skills, giving models transfer and generalization capabilities and breaking down barriers to data reuse;
• Comprehensive: Encompassing full ground-truth values across vision, language, tactile, and action modalities, facilitating pre-training modality alignment;
• Massive: Scale comparable to large language models, ensuring the future potential of embodied intelligence.

Based on these four core characteristics, the Tashi WIYH dataset offers three unique advantages:

First, in modality completeness: through a self-developed collection kit, it simultaneously captures vision (RGB), force-tactile (pressure sensor signals), and action (finger joint poses and end-effector trajectories) data, ensuring precise temporal and spatial alignment of multi-source data.

Second, in data annotation pipeline: WIYH leverages its own cloud-based foundation model for high-precision annotation, covering multi-granularity ground-truth labels including 2D semantics, scene depth, operational task decomposition, object affordance, and hand and end-effector motion trajectories — providing comprehensive, multidimensional supervisory signals for embodied foundation model pre-training.

Finally, in collection environment: Tashi goes deep into real-life operational scenarios. Compared to the industry's typical high-cost proprietary data collection and training factories, it collects standard operating procedure data from workers in non-constructed, non-dedicated, non-enclosed environments — significantly improving data authenticity, diversity, and generalization while reducing collection costs by more than an order of magnitude.

A supermarket worker wearing a self-developed collection device restocks shelves

Tashi Zhihang's World In Your Hands marks the establishment of a Human-Centric embodied data paradigm. It makes real-world embodied AI World Engine pre-training possible. Grounded in "thousands of industries," WIYH aims to achieve "one model, thousand tasks," becoming critical corpus and infrastructure for training general embodied foundation models — pushing industry applications from single-task operations toward a new stage of general manipulation capability, and laying a solid foundation for embodied robots to truly enter millions of businesses and homes.

Tashi Zhihang is committed to providing the industry with the highest-quality robot bodies, data, and model solutions. The Tashi WIYH dataset is scheduled to open in December 2025. Research institutions and partners are welcome to collaborate with Tashi in building an open and thriving embodied intelligence ecosystem, ushering in a new chapter of general intelligence.

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