Self-driving laboratories (SDLs) close the loop between experiment design, automated execution, and data-driven decision making, and they provide a demanding testbed for agentic AI under expensive actions, noisy and delayed feedback, strict feasibility and safety constraints, and non-stationarity. This survey uses soft matter as a representative setting but focuses on the AI questions that arise in real laboratories. We frame SDL autonomy as an agent environment interaction problem with explicit observations, actions, costs, and constraints, and we use this formulation to connect common SDL pipelines to established AI principles. We review the main method families that enable closed loop experimentation, including Bayesian optimization and active learning for sample efficient experiment selection, planning and reinforcement learning for long horizon protocol optimization, and tool using agents that orchestrate heterogeneous instruments and software. We emphasize verifiable and provenance aware policies that support debugging, reproducibility, and safe operation. We then propose a capability driven taxonomy that organizes systems by decision horizon, uncertainty modeling, action parameterization, constraint handling, failure recovery, and human involvement. To enable meaningful comparison, we synthesize benchmark task templates and evaluation metrics that prioritize cost aware performance, robustness to drift, constraint violation behavior, and reproducibility. Finally, we distill lessons from deployed SDLs and outline open challenges in multi-modal representation, calibrated uncertainty, safe exploration, and shared benchmark infrastructure.

翻译：自驱实验室（SDL）实现了实验设计、自动化执行与数据驱动决策的闭环，为在昂贵操作、噪声与延迟反馈、严格可行性与安全约束以及非平稳性条件下的自主AI提供了严苛的测试平台。本综述以软物质为代表性场景，但聚焦于真实实验室中产生的AI问题。我们将SDL自主性框架化为具有显式观测、行动、成本与约束的智能体-环境交互问题，并利用此框架将常见SDL流程与既有AI原理相连接。我们回顾了实现闭环实验的主要方法体系，包括用于高效样本实验选择的贝叶斯优化与主动学习、用于长时程协议优化的规划与强化学习，以及协调异构仪器与软件的工具调用智能体。我们强调支持调试、可复现性与安全运行的可验证且具备溯源意识的策略。随后，我们提出一种能力驱动的分类体系，通过决策时域、不确定性建模、行动参数化、约束处理、故障恢复及人机协同维度对系统进行组织。为促成有效比较，我们综合了基准任务模板与评估指标，优先考量成本感知性能、对漂移的鲁棒性、约束违反行为及可复现性。最后，我们从已部署的SDL中提炼经验，并展望了多模态表征、校准不确定性、安全探索及共享基准设施等领域的开放挑战。