让机器人通过学习动作偏好，智能选择最佳操作位置。
N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
- 从动作执行的轨迹中学习位置偏好，而非依赖几何可达性。
- 在三个真实场景数据集上提升成功率15%-23%，优于现有方法。
- 无需预先建模环境，适合快速部署到新场景的机器人系统。
确定移动操作中的执行位置是核心挑战。传统方法将其视为几何搜索问题，仅关注物理可达性。然而，现代基于学习的操作策略对位置极为敏感，仅靠几何标准无法实现最优性能。理想的位置应考虑策略自身的偏好。尽管已有研究尝试解决该问题，但受限于依赖预构建场景重建和推理缓慢，实用性不足。本文提出N2M，系统性重构基座定位问题，自然克服了先前方法的局限。关键洞察是：策略偏好源自局部场景结构，可从策略采样轨迹中有效学习。技术上，我们设计一种新型视角增强策略，使模型以极高的数据效率学习到视角不变的姿势偏好。大量实验表明，N2M在多个真实场景数据集上达到领先性能，显著优于非策略感知基线及近期策略感知方法。此外，我们进行了全面分析，验证其广泛适用性、泛化能力与数据效率。项目主页：https://clvrai.github.io/N2M/
原文摘要 · Abstract (English)
Determining where to execute the manipulation policy is a fundamental challenge in mobile manipulation. Most approaches have formulated this as a geometric search problem, prioritizing physical reachability. However, given the high sensitivity of modern learning-based manipulation policies, geometric criteria alone are insufficient. Optimal performance requires base positioning that is aware of the policy's preference. While recent works have attempted to address this, they remain limited in practicality due to reliance on pre-built scene reconstruction and slow inference. In this work, we introduce N2M that systematically reformulates the approach to base positioning problem, naturally overcoming limitations of previous methods. Our key insight is that policy preferences are inherent to the local scene structure and can be effectively learned from the policy rollouts. Technically, we propose a novel viewpoint augmentation strategy that enables the model to learn robust, viewpoint-invariant pose preferences with remarkable data efficiency. Extensive experiments demonstrate that N2M achieves state-of-the-art performance, outperforming both non-policy-aware baselines and recent policy-aware alternatives. Furthermore, we provide a comprehensive analysis highlighting N2M's broad applicability, generalization capabilities, and data efficiency. Project website: https://clvrai.github.io/N2M/
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