Spranic builds spatial memory for physical AI
Physical AI needs context about its surroundings to adapt beyond a fixed set of instructions. Munich-based Spranic is building a spatial memory layer that records physical spaces and how they change. Connecting that information with knowledge graphs could help robots interpret their environments across different tasks, according to Jordi Spranger (pictured), founder and chief executive […] The post Spranic builds spatial memory for physical AI appeared first on SiliconANGLE.
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Physical AI robots currently struggle to adapt to changing environments without task-specific instructions. A Munich-based company is developing a spatial memory layer that records physical spaces and their changes, which can be connected with knowledge graphs to help robots interpret their surroundings across different tasks. Knowledge graphs enable robots to access relevant information from multiple sources while preserving the origin of that data, making their decisions traceable and auditable. This approach allows robots to operate flexibly across varied scenarios rather than being limited to pre-planned, fixed tasks.
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