Semantic- and Density-Aware Planning for Accessibility-Preserving Multi-Object Placement

Benno Wingender; Nils Dengler; Nicolas Busch; Sicong Pan; Maren Bennewitz

cs.ROAug 17, 2026

Semantic- and Density-Aware Planning for Accessibility-Preserving Multi-Object Placement

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.

Figures & tables

Fig. 1 : Overview of the online multi-object shelf-placement task and the proposed approach. Starting from a partially occupied multi-level shelf, objects are observed and placed one at a time without knowledge of future arrivals. At each step, candidate placements on the selected shelf level are evaluated according to semantic-dense organization and accessibility preservation, after which the highest-ranked feasible placement is executed. This process is repeated until no further object can be placed, producing a dense and semantically organized shelf arrangement.

Fig. 2 : Overview of our semantic-dense placement planning pipeline. The perceived shelf environment is converted into 2D occupancy map for each shelf level (left), from which the Accessibility Map (AM) identifies occupied (gray) and manipulation-inaccessible (black) regions (green panel). Candidate placement poses are sampled in the remaining workspace and evaluated using a semantic-density score, which combines GloVe-based [ 24 ] object similarity with spatial proximity (orange panel), and a space-preservation penalty (blue panel) that estimates the loss of accessible shelf space caused by each placement pose. The combined score ranks candidate placements poses. pairs release and grasp pose candidates, and then sequentially checks for a collision-free pick-and-place trajectory (right).

Fig. 3 : Pairwise semantic similarity matrix computed from GloVe embeddings [ 24 ] after contrast normalization. Warmer colors indicate semantic similarity, while cooler colors denote dissimilarity.

Panel of Figure 4 — Fig. 4 : Evolution of shelf-arrangement metrics over successive placements, averaged over 10 paired trials. Each trial starts with 12 objects, denoted as placement step 0, and terminates when no executable placement can be found for the current object. Panels (a)–(c) report the semantic score, proximity score, and shelf density, respectively. SDPP maintains the strongest semantic organization as the shelf is filled while achieving shelf utilization comparable to the strongest density-oriented baselines.

Ablations
Variant	Add. Obj. Placed $\uparrow$	Semantic Sum $\uparrow$	Semantic $\uparrow$	Proximity $\uparrow$	Density $\uparrow$
Ours	$\mathbf{30.7\pm 3.9}$	$\mathbf{61.6\pm 11.5}$	$\mathbf{1.440\pm 0.180}$	$\mathbf{0.788\pm 0.036}$	$\mathbf{0.261\pm 0.016}$
SPS [ 13 ]	$4.7\pm 1.5$	$14.1\pm 1.9$	$0.855\pm 0.083$	$0.428\pm 0.095$	$0.100\pm 0.010$
SPS++ [ 13 ]	$28.2\pm 3.6$	$48.7\pm 10.0$	$1.208\pm 0.163$	$0.718\pm 0.041$	$0.245\pm 0.018$
Dengler [ 29 ]	$28.6\pm 4.6$	$32.4\pm 4.4$	$0.802\pm 0.065$	$0.557\pm 0.042$	$0.248\pm 0.018^{\dagger}$
Random	$19.9\pm 2.7$	$25.4\pm 3.1$	$0.799\pm 0.041$	$0.505\pm 0.047$	$0.193\pm 0.018$

TABLE I: Comparison of SDPP with the evaluated baselines and ablations. Each shelf initially contains 12 objects; Add. Obj. Placed denotes the number of additional objects successfully placed. All methods are evaluated over 10 paired trials using the same initial shelf configurations and incoming-object sequences. Values are reported as mean

\pm

standard deviation, and bold indicates the best result among the main methods. The semantic sum is computed over all objects in the final shelf, whereas the semantic, proximity, and density scores are averaged per object. Paired Wilcoxon signed-rank tests show significant differences between SDPP and each baseline for all reported metrics (

p<0.05

), except for shelf density compared with Dengler [ 29 ] , marked by † . No statistical significance tests are reported for the ablation variants. Among the main methods, SDPP achieves the strongest overall performance, combining the best semantic organization with the highest placement capacity and average shelf density. The ablations show that increasing the accessibility-preservation weight improves placement capacity and density, but eventually trades off semantic quality.

Fig. 5 : Mean accumulated IK and motion-planning time required to identify the first executable placement as the shelf is progressively filled, averaged over 20 runs. The no-filter baseline is shown in lime, while the AM-based strategies are shown in yellow (FC), pink (CC), and blue (FC $\rightarrow$ CC), where FC is followed by CC if no feasible pose is found. The red curve indicates the remaining accessible shelf area. AM-based filtering substantially reduces the candidate-search time, while FC $\rightarrow$ CC retains a less restrictive fallback when FC rejects all feasible candidates.

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Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning

Lance Pharand, Megnath Ramesh, Mark Chiu, Yash Vardhan Pant+1

Electrical and Computer Engineering Department at the University of Waterloo, ON, Canada

Non-Prehensile Manipulation Mobile Robotics Diffusion-Based Planning

Open paper AskPDF arXiv

Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions. Precision placement has been studied with manipulators on benchmarks such as Push-T, where an omnidirectional end effector can readily re-approach an object from any side. For a mobile robot, correcting the same placement errors may require wide repositioning maneuvers around the object. We formalize multi-object pushing with a mobile robot as a multi-target assignment problem at tight tolerances. We propose a diffusion planner with a causal factorization: each robot denoising step is followed by an object step conditioned on the current clean robot estimate. This factorization allows us to sample multiple object outcomes for a single robot plan, capturing the multi-modality of pushing. We also design a set of pushing-specific costs for mobile robots, used for both guidance and best-of-

N

sampling at inference. On Push-T, our planner attains the highest success rate of the methods we compare, while having the lowest rate of implausible object motion. Across three multi-box pushing environments resembling warehouse scenarios, it raises the fraction of boxes placed by

21.5

percentage points over an unguided baseline, and replans

1.8\times

faster than a planner that denoises the two trajectories together. Lastly, we deploy the guided planner on a TurtleBot 3 Burger with object shapes absent from the training data, where it places

85.0\%

of objects within tolerance against Diffusion Policy's

37.5\%

Abstract

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