脑类器官计算驱动的机器人决策
Brain organoid computing for robotic decision-making
bioRxiv · 2026 年 9 月 15 日 · Hongwei Cai, Chunhui Tian, Yang Yang 等 16 人
现有机器人在复杂动态环境中的高效决策能力仍远不及人脑。研究者提出Brainobot，一种生物混合机器人系统，用脑类器官（由干细胞培养出的微型脑组织）作为储备池计算（一种利用复杂动力系统处理信息的计算范式）控制器，充当机器人的高层决策层，接收感觉输入并生成运动指令。作为概念验证，该系统部署于人形机器人完成物体抓取和激光追踪等真实任务，并表现出跨任务适应性、高计算效率和低能耗等特点。作者认为这一路径可为机器人具身智能和生物决策机制研究提供启示。该研究为预印本，尚未经同行评审，其涉及的人脑类器官伦理问题值得关注。
为什么推荐给您：首次将脑类器官作为机器人高层决策控制器并完成真实任务，属新模态概念验证，但为预印本且仍处早期。
不需要生物学背景，多打比方
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摘要Abstract
Biomimicry has inspired the evolution of robotics toward greater autonomy, adaptability, and symbiosis with humans and dynamic environments. 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.