具脑磐石发布业界首个类脑认知世界模型 Cog-WM 1.0

TL;DR - EBKernel launched Cog-WM 1.0, a brain-inspired world model for robotic navigation and manipulation that combines latent-space prediction, spatiotemporal memory, and goal/value guidance. Reported benchmark gains suggest a potential path toward robots requiring less training data and fewer prebuilt environmental maps.

• Cog-WM Nav 1.0 navigates without prebuilt maps; on an HM3D-ObjectNav subset, success rose from BSC-Nav’s 78.50% to 86.89%, while SPL increased from 47.70 to 48.35.
• Cog-WM Manip 1.0 uses multi-timescale state prediction and value-guided experience learning, reportedly outperforming large-scale pretrained baselines across three manipulation benchmarks by as much as 16%.
• The shared architecture predicts task-relevant abstract representations rather than pixels and separates reusable spatial structure from memory content.
• The system has been validated on wheeled humanoid and quadruped robots, including navigation, path planning, memory retrieval, spatial question answering, object search, and manipulation.