AI 中文总结
针对在线多物体货架放置场景，提出SDPP方法，结合语义-密度评分与AM，在提升语义放置质量和货架密度的同时，减少可行位姿识别时间，适用于家庭货架存储场景。
AI 中文摘要
长期操作规划要求机器人不仅要考虑即时任务成功，还要推理当前决策如何影响未来与环境的交互。在此背景下，家庭服务机器人可能需要在部分占用的货架上整理杂货，同时高效利用有限的存储空间，并为后续放置保留可访问性。本文考虑一种在线多物体货架放置场景，其中未来物体的到达是未知的。现有方法无法在连续货架填充过程中同时解决语义组织、密集空间利用和机械臂可访问性问题。为填补这一空白，我们提出语义-密集放置规划（Semantic-Dense Placement Planning, SDPP），这是一种保持可访问性的方法，它使用语义-密度评分对候选位姿进行排序，该评分结合了物体间的语义相似性与空间邻近性。可访问性地图（Accessibility Map, AM）进一步过滤在运动规划前不太可能到达的候选位姿，并惩罚那些减少剩余可访问工作空间的放置。仿真实验表明，与最先进的基线方法相比，SDPP显著提高了语义放置质量，实现了最高的平均货架密度，而AM大幅减少了识别可行放置位姿所需的时间。一项定性的真实世界实验证明了我们的流程在家庭货架存储场景中的适用性。
英文摘要
Long-term manipulation planning requires robots to reason not only about immediate task success but also about how current decisions affect future interactions with the environment. In this context, household service robots may need to organize groceries in partially occupied shelves while using limited storage space efficiently and preserving access for subsequent placements. In this paper, we consider an online multi-object shelf-placement setting in which future objects arrivals are unknown. Existing approaches do not jointly address semantic organization, dense space utilization, and manipulator accessibility during sequential shelf filling. To address this gap, we propose Semantic-Dense Placement Planning (SDPP), an accessibility-preserving approach that ranks candidate poses using a semantic-density score combining inter-object semantic similarity with spatial proximity. An Accessibility Map (AM) further filters candidates unlikely to be reachable before motion planning and penalizes placements that reduce the remaining accessible workspace. Simulation experiments show that SDPP significantly improves semantic placement quality over state-of-the-art baselines and achieves the highest average shelf density, while the AM substantially reduces the time required to identify feasible placement poses. A qualitative real-world experiment demonstrates the applicability of our pipeline in a domestic shelf-storage scenario.
