Semantic- and Density-Aware Planning for Accessibility-Preserving Multi-Object Placement
2026-08-17 • Robotics
Robotics
AI summaryⓘ
The authors study how robots can better organize groceries on shelves when they don't know what items will come next. They created a method called Semantic-Dense Placement Planning (SDPP) that helps the robot decide where to put items by considering both how similar items are and how close they are on the shelf, while keeping future access possible. Their system also uses an Accessibility Map to avoid placing items in hard-to-reach spots. Tests in simulations showed that their approach improves organization and shelf space use and works efficiently, and a real-world test showed it can be used at home.
manipulation planningsemantic organizationspace utilizationrobotic shelf storageonline multi-object placementaccessibility mapmotion planningsemantic similarityrobotic grasping
Authors
Benno Wingender, Nils Dengler, Nicolas Busch, Sicong Pan, Maren Bennewitz
Abstract
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.