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Spranic is developing a spatial memory layer that combines environmental mapping with knowledge graphs to enable physical AI agents to adapt dynamically to changing real-world contexts. This approach addresses the critical challenge of reducing task-specific planning for robots by providing them with reusable, auditable context similar to advanced digital navigation systems. By integrating data provenance across domains, this technology aims to make autonomous systems more flexible and safe in unstructured environments.
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.
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),…
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