The　efficient　disassembly　of　end-of-life　electric　vehicle　batteries(EOL-EVBs)　is　crucial　for　green　manufacturing　and　sustainable　development.　The　current　pre-programmed　disassembly　conducted　by　the　Autonomous　Mobile　Manipulator　Robot(AMMR)　struggles　to　meet　the　disassembly　requirements　in　dynamic　environments,　complex　scenarios,　and　unstructured　processes.　In　this　paper,　we　propose　a　Battery　Disassembly　AMMR(BEAM-1)　system　based　on　NeuralSymbolic　AI.　It　detects　the　environmental　state　by　leveraging　a　combination　of　multi-sensors　and　neural　predicates　and　then　translates　this　information　into　a　quasi-symbolic　space.　In　real-time,　it　identifies　the　optimal　sequence　of　action　primitives　through　LLM-heuristic　tree　search,　ensuring　high-precision　execution　of　these　primitives.　Additionally,　it　employs　positional　speculative　sampling　using　intuitive　networks　and　achieves　the　disassembly　of　various　bolt　types　with　a　meticulously　designed　end-effector.　Importantly,　BEAM-1　is　a　continuously　learning　embodied　intelligence　system　capable　of　subjective　reasoning　like　a　human,　and　possessing　intuition.　A　large　number　of　real　scene　experiments　have　proved　that　it　can　autonomously　perceive,　decide,　and　execute　to　complete　the　continuous　disassembly　of　bolts　in　multiple,　multi-category,　and　complex　situations,　with　a　success　rate　of　98.78%.　This　research　attempts　to　use　NeuroSymbolic　AI　to　give　robots　real　autonomous　reasoning,　planning,　and　learning　capabilities.　BEAM-1　realizes　the　revolution　of　battery　disassembly.　Its　framework　can　be　easily　ported　to　any　robotic　system　to　realize　different　application　scenarios,　which　provides　a　ground-breaking　idea　for　the　design　and　implementation　of　future　embodied　intelligent　robotic　systems.　©　2024　IEEE.