arXiv 2610.27851 · cs.CV · 2026-10-20
LaLa-World: Scaling Latent World Models with Natural Language Actions
Junha Song, Geonmo Gu, Jeongeun Park, Heejeong Nam, Lucas Maes, Byeongho Heo, Dongyoon Han, Sangdoo Yun
NAVER AI Lab
在最新榜 #288 本站收录于 2026-10-24
一句话要点
NAVER AI Lab 的 LaLa-World 是用自然语言描述动作的 JEPA 式潜在世界模型：把数值动作和画面变化都写成语言，从而用一个未来预测器联合学习仿真、机器人轨迹和更广泛的图像视频数据，用于“想象中规划”和下游策略微调。
NAVER AI Lab's LaLa-World is a JEPA-style latent world model with a language-conditioned interface: describing both numerical actions and visual changes in language lets one future predictor train jointly on simulation, robot trajectories and broader image/video data, for planning in imagination and policy fine-tuning.
阅读 arXiv 原文 PDF Hugging Face 讨论 代码仓库 项目页
主题与学科
同主题：机器人与具身
- Has GPT-6-Astra Achieved Embodied Intelligence? ▲421
- In-Context Learning for Robots: Methods and Applications ▲388
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives ▲310
- HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents ▲294
- Reflex: Visually Grounded Reactive Humanoid Control ▲154
同主题：视觉生成
- StreamEval: Evaluating Interactive Streaming Video Generation Models 2026-10-20 · ▲649
- MaLiang-Harness: A Programmable Path to Image and Video Generation 2026-09-28 · ▲367
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation 2026-10-14 · ▲364
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models 2026-10-08 · ▲325
- AgentGarten: Code Worlds for Evolving Agents 2026-10-08 · ▲293
同机构：NAVER AI Lab
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-26。
要点由本站根据摘要自写，不转载摘要原文，请阅读原文。