MANIPULATION
世界场景策略2.0：通过推理增强记忆赋能可操控的世界动作建模
Haisheng Su, Zongdai Liu, Xin Jin, Haoxuan Dou, Chengming Hu, Baorun Li, Zhanwang Liu, Ruiyan Xu, Jianjie Fang, Xin Zhang, Zhenjie Yang, Xue Yang, Chen Gao, Junchi Yan, Yong Li, Wei Wu
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出WorldScape Policy 2.0，一种具有推理增强长短时记忆的可控世界动作模型，通过因果短期视觉记忆和长短时事件记忆实现精细的多模态操控。该方法利用检索历史增强感知和规划令牌生成，实现自主规划与跨实体迁移，显著提升了机器人操控的细粒度可控性。
关键词
World Action Modelrobotic manipulationlong short-term memorymultimodal groundingautonomous planning
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