Yingjie Technology (映界科技), a Chinese embodied-AI startup, released a new spatial-memory framework that combines 4D Gaussian Splatting with a Vision-Language Model (VLM) to give embodied robots a "perceivable layer" that can be queried by LLMs.
The technical details: the framework has three components — (1) a "4D Gaussian scene" — a dynamic 3D representation of the environment, with each Gaussian carrying semantic information (e.g., "this is a chair," "this is a door handle"); (2) a "VLM grounding" — a Vision-Language Model that maps natural-language queries to specific Gaussians (e.g., "the chair near the window" → specific Gaussian cluster); (3) an "LLM interface" — a structured API that allows the robot's LLM planner to query the spatial memory ("what objects are in the kitchen?" → list of relevant Gaussians).
The "queryable spatial memory" highlight: the framework allows an LLM to ask "what is in the living room?" and get a structured answer with spatial coordinates. The robot can then plan a path to those objects. This is a significant improvement over traditional SLAM-based spatial memory, which only provides geometric information, not semantic information.
The benchmark: on a set of embodied-AI tasks ("find the red cup," "navigate to the kitchen"), the framework improves success rate by 35% over traditional SLAM-based approaches. The biggest improvement is on tasks that require semantic understanding ("the cup that John was using yesterday"), which traditional SLAM cannot handle.
The bigger takeaway: "semantic spatial memory" is the missing layer in embodied AI. Current robots have either geometric memory (SLAM) or language understanding (LLM), but not both. The 4D Gaussian + VLM combination is the right architecture, and it will likely be adopted by other embodied-AI vendors. For the industry, this signals that "spatial memory as a service" is a new product category.