人工智能与机器人科学家在科学发现中的规模定律

Scaling Laws in Scientific Discovery with AI and Robot Scientists

March 28, 2025

作者: Pengsong Zhang, Heng Zhang, Huazhe Xu, Renjun Xu, Zhenting Wang, Cong Wang, Animesh Garg, Zhibin Li, Arash Ajoudani, Xinyu Liu

cs.AI

摘要

科学发现正通过先进的机器人技术和人工智能迎来快速发展的契机。当前的科学实践面临显著局限，手工实验既耗时又耗费资源，而跨学科研究则要求整合超出个体研究者专业范围的知识。在此，我们构想了一种自主全能科学家（AGS）的概念，它将代理型人工智能与具身机器人技术相结合，旨在自动化整个研究生命周期。该系统能够动态地与物理和虚拟环境互动，同时促进跨学科知识的融合。通过在研究的各个阶段——从文献综述、假设生成、实验到论文撰写——部署这些技术，并结合内部反思与外部反馈，该系统有望大幅减少科学发现所需的时间和资源。基于从虚拟AI科学家向多功能通用型AI机器人科学家的演进，AGS展现出突破性的潜力。随着这些自主系统日益融入研究过程，我们推测科学发现可能遵循新的规模定律，这些定律或许由这些自主系统的数量与能力所塑造，从而为知识的生成与演变提供全新视角。具身机器人对极端环境的适应能力，加上科学知识积累的飞轮效应，预示着持续突破物理与智力边界的无限可能。

English

Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual experimentation remains time-consuming and resource-intensive, while multidisciplinary research demands knowledge integration beyond individual researchers' expertise boundaries. Here, we envision an autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. This system could dynamically interact with both physical and virtual environments while facilitating the integration of knowledge across diverse scientific disciplines. By deploying these technologies throughout every research stage -- spanning literature review, hypothesis generation, experimentation, and manuscript writing -- and incorporating internal reflection alongside external feedback, this system aims to significantly reduce the time and resources needed for scientific discovery. Building on the evolution from virtual AI scientists to versatile generalist AI-based robot scientists, AGS promises groundbreaking potential. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws, potentially shaped by the number and capabilities of these autonomous systems, offering novel perspectives on how knowledge is generated and evolves. The adaptability of embodied robots to extreme environments, paired with the flywheel effect of accumulating scientific knowledge, holds the promise of continually pushing beyond both physical and intellectual frontiers.