北京商报讯（记者 陶凤 王天逸）1月29日，宇树宣布开源UnifoLM-VLA-0。 资料显示，UnifoLM-VLA-0是UnifoLM系列下面向通用人形机器人操作的视觉-语言-动作（VLA）大模 型。该模型旨在突破传统VLM在物理交互中的局限，通过在机器人操作数据上的继续预训练，实现了 从通用"图文理解"向具备物理常识的"具身大脑"的进化。 ...

宇树在UnifoLM-VLM-O模型的基础上集成了动作预测头(Action Head)，从而构建出Uni-foLM-VLA-0。 经由仿真环境与真机实验的多任务训练验证，结果显示该模型具备单模型处理多任务的通用能力，在 LIBERO仿真基准测试中，公司的多任务模型取得了接近最优的性能。 在宇树G1人形机器人平台上，宇树科技团队构建了覆盖12类复杂操作任务的高质量真机数据集，并基 于此对UnifoLM-VLA-0进行单一策略网络的统一端到端训练。真机实验结果表明，该模型能够在同一 策略checkpoint下，稳定完成全部12项任务，在外部扰动条件下仍保持良好的执行鲁棒性与抗干扰能 力。 智通财经APP获悉，1月29日，宇树科技官方账号宣布开源UnifoLM系列下面向通用人形机器人操作的 视觉-语言-动作(VLA)大模型"UnifoLM-VLA-0"。该模型旨在突破传统VLLM在物理交互中的局限，通 过在机器人操作数据上的继续预训练，实现了从通用"图文理解"向具备物理常识的"具身大脑"的进化。 该模型在多类任务场景下展现出显著增强的空间推理能力与可靠的多模态感知性能。针对操作类任务中 对指令理解与空间感知的高要求 ...

每经AI快讯，1月29日，宇树宣布开源 UnifoLM-VLA-0。UnifoLM-VLA-O是UnifoLM系列下面向通用人 形机器人操作的视觉-语言-动作（VLA）大模型。该模型旨在突破传统VLM在物理交互中的局限，通过 在机器人操作数据上的继续预训练，实现了从通用"图文理解"向具备物理常识的"具身大脑"的进化。 ...

人民财讯1月29日电，宇树1月29日宣布，开源UnifoLM-VLA-0。UnifoLM-VLA-0是UnifoLM系列下面向 通用人形机器人操作的视觉-语言-动作（VLA）大模型。该模型旨在突破传统VLM在物理交互中的局 限，通过在机器人操作数据上的继续预训练，实现了从通用"图文理解"向具备物理常识的"具身大脑"的 进化。 ...

Investment Rating - The report does not explicitly state an investment rating for the smart driving industry Core Insights - The smart driving industry is experiencing rapid evolution driven by "end-to-end" and "smart driving equity" concepts, with significant growth in both new energy vehicle sales and smart driving functionalities [3][4][9] - The penetration rate of L2-level smart driving in new energy vehicles in China has increased from approximately 7% in 2019 to around 65% by the first half of 2025, indicating a strong correlation between new energy vehicle sales and the adoption of smart driving technologies [9][10] - The smart driving market is projected to exceed 5 trillion yuan by 2030, with a compound annual growth rate driven by technological advancements and increased consumer acceptance [15][16] Summary by Sections 1. "Equity + End-to-End" Accelerating Smart Driving Evolution - The smart driving industry has seen a significant increase in new energy vehicle sales, which has created a positive feedback loop for the adoption of smart driving technologies [9][10] - The penetration of L2-level smart driving features in new energy vehicles has rapidly increased, reflecting the growing consumer acceptance and market expansion of smart driving technologies [9][10] 2. End-to-End Smart Driving Review - The evolution of end-to-end smart driving can be categorized into four main stages, with advancements in perception, decision-making, and control processes [30][32] - The introduction of the "occupancy network" has enhanced environmental perception capabilities, allowing for more accurate and stable decision-making in complex driving scenarios [46][47] 3. VLA Technology Route - The VLA (Vision-Language-Action) model is emerging as a key driver of paradigm shifts in autonomous driving, integrating visual, linguistic, and action modalities into a cohesive framework [70][71] - The VLA model's development is divided into four stages, with significant advancements in task understanding and execution capabilities [76][77] 4. World Model Technology Route - The world model approach emphasizes physical reasoning and spatial understanding, representing a long-term evolution path for smart driving technologies [69][70] - The integration of world models with cloud computing is expected to enhance the iterative optimization of end-to-end smart driving systems [65][66]