发表机构
Department of Mechanical of Automation Engineering（机械与自动化工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究可泛化机器人操作中功能对象部分的稳定3D理解问题，提出以对象为中心的连续语义场，用部分注释模型训练并冻结，实验表明该表示提供更稳定线索并提升策略性能。
AI 中文摘要
可泛化机器人操作需要对功能对象部分（如手柄、工具头、开口和可抓握区域）有稳定的3D理解。原始点云提供几何信息但缺乏明确的部分语义，现有方法的特征依赖观察样本。我们提出以对象为中心的连续语义场，经训练后为操作策略生成语义点云，实验证明其优势。
英文摘要
Robotic manipulation often requires identifying functional parts, such as a mug handle or a hammer head. However, features attached to observed 3D points can vary with viewpoint and sensor noise, giving a policy inconsistent representations of the same part. We propose an object-centric semantic field to provide more consistent part-aware features for manipulation. We use the observed object cloud to build a continuous field, then read features from this field at 3D locations independently resampled from the cloud. Each feature uses the sampled object support as context, rather than directly reusing an individual point descriptor. Part classification distinguishes functional regions, cross-instance alignment brings corresponding part features together, and perturbation consistency encourages similar features under observation changes. The queried coordinates and features form semantic point clouds that are supplied to a DP3-based policy. We evaluate the approach on four RoboTwin simulation tasks and four real-world bimanual tasks, achieving average success rates of 69.3\% and 67.5\%, respectively. These improve on Utonia Point-wise by 7.0 and 32.5 percentage points, respectively, with real-world tests on held-out objects. A point-wise control with matched part supervision scores 63.5\% in simulation, compared with our 69.3\%. These results highlight the value of stable, object-conditioned semantic fields for manipulation across object instances and varying observations. Project Page: \href{https://zainzh.github.io/beyond-point-attached-semantics}{https://zainzh.github.io/beyond-point-attached-semantics}.
