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However, current robotic systems still face major challenges in recapitulating the high-efficiency decision-making capabilities of the human brain under complex and dynamic conditions. Here, we present Brainobot, a biohybrid robotic system that establishes a brain organoid controller as a high-level robotic decision-making layer for closed-loop embodiment. By leveraging brain organoid reservoir computing, Brainobot interacts with dynamic environments by receiving and processing sensory inputs and generating motor actions. As a proof-of-concept demonstration, Brainobot is implemented in a humanoid robotic system to perform real-world tasks, including object grasping and laser chasing. Interestingly, Brainobot exhibits unique features, including cross-task adaptivity, high computing efficiency, and low energy consumption. Thus, our approach may provide insights for advancing robotic embodiment and understanding biological decision-making.<\/jats:p>","DOI":"10.64898\/2026.09.09.750426","type":"posted-content","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T23:10:15Z","timestamp":1789513815000},"source":"Crossref","is-referenced-by-count":0,"title":["Brain organoid computing for robotic decision-making"],"prefix":"10.64898","author":[{"given":"Hongwei","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Intelligent Systems Engineering, Indiana University Bloomington, IN, 47405, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhui","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems Engineering, Indiana University Bloomington, IN, 47405, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department 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