外观
论文arXiv 2610.2339612 天前
AWAM：用于稳健机器人操作的主动轨迹条件视角想象
原文链接：AWAM: Active Trajectory-Conditioned View Imagination for Robust Robot Manipulation（arXiv 2610.23396 · 发布日期：2026-10-16）
AWAM: Active Trajectory-Conditioned View Imagination for Robust Robot Manipulation
AWAM通过主动规划观察并沿轨迹想象视图，结合世界模型与扩散动作预测，在仿真基准和真机试验验证稳健操作。
核心要点
- 开放世界中的杂乱、遮挡和视角变化会削弱动作所需空间关系感知。
- 模型预测任务相关虚拟相机运动，查询几何条件世界模型生成空间先验。
- 空间先验压缩为固定记忆，并与实时观测交叉注意力融合用于扩散动作预测。
- 在多个仿真基准和真机试验取得竞争成功率，支持提升所评估遮挡与分布偏移鲁棒性。
验证范围与局限：验证范围为LIBERO-Occ、LIBERO-Plus、RLBench-OG Occlusion1及真机试验中的遮挡和分布偏移。
英文摘要 (Abstract)
Rapid progress in embodied intelligence has enabled increasingly capable robots to follow language instructions and manipulate diverse objects. However, strong results in structured environments do not automatically transfer to open-world conditions, where clutter, occlusion, and changing viewpoints can obscure the spatial relationships required for action. To address this perceptual gap, a virtual active-perception paradigm is introduced. AWAM, an Active World-Action Model actively plans where to observe and imagines what will be seen along the planned trajectory before acting. Task-related virtual camera motion is predicted and used as a structured query into a geometry-conditioned world model, producing a reusable spatial prior for manipulation, which is later compressed into a fixed memory and fused with live observations through cross-attention for diffusion-based action prediction. AWAM achieves competitive success rates with 75.9% on LIBERO-Occ, 62.4% on LIBERO-Plus, 76.5% on RLBench-OG Occlusion1, and 94.4% in real-robot trials. These results support improved robustness to the evaluated occlusions and distribution shifts, addressing part of the gap between structured manipulation and less controlled environments.
作者：Jiawei Geng, Zhenyang Liu, Yuzheng Wu, Yanwei Fu
来源
本页事实对应的公开页面，以原文为准。