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Learning Spatial-Aware Manipulation Ordering
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Proposes OrderMind, a unified RGB-D model that predicts per-object manipulation priority scores in cluttered scenes via kNN spatial graphs and attention, trained with labels from a VLM guided by spatial priors, achieving real-time ordering.
Problem
Determining a safe, efficient manipulation order for objects in cluttered scenes, where spatial dependencies cause collisions or blocked access, while existing methods are heuristic or slow VLM-based pipelines.
Approach
A unified network that jointly encodes object-object (kNN spatial graph) and object-manipulator relations with a temporal priority structuring attention module, outputting continuous priority scores trained by a pairwise ranking loss on VLM-generated orders guided by independence and local-optimality spatial priors.
Key results
- OrderMind achieves 96.5% success rate on easy, 95.3% on moderate and 95.4% on hard simulation settings, exceeding privileged VLM baselines such as Gemini-2.5 (92.4/84.6/78.5%).
- OrderMind runs at 5.6 FPS with 41.8M parameters, while OrderMind-Mini reaches 21.3 FPS with 35.2M parameters versus 0.1 FPS for two-stage VLM pipelines.
- Real-world closed-loop success rates are 93.3% (easy), 78.5% (moderate) and 76.6% (hard).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use OrderMind instead of the strongest reported VLM baseline | Simulation Easy · Simulation Easy | success rate (%): 92.5$\pm$10.3 -> 96.5{${\pm }$}5.3 | Helped |
| Use OrderMind instead of Gemini-2.5 | Simulation Moderate · Simulation Moderate | success rate (%): 84.6$\pm$10.8 -> 95.3{${\pm }$}5.1 | Helped |
| Use OrderMind instead of Gemini-2.5 | Simulation Hard · Simulation Hard | success rate (%): 78.5$\pm$16.5 -> 95.4{${\pm }$}5.1 | Helped |
| Add spatial context understanding (SCU) (ablation) | OrderMind baseline without SCU, TPS, or SPOL · Simulation Moderate | success rate (%): 76.1 -> 81.0 | Helped |
| Add temporal priority structuring (TPS) (ablation) | OrderMind baseline without SCU, TPS, or SPOL · Simulation Moderate | success rate (%): 76.1 -> 80.5 | Helped |
| Add TPS to SCU (ablation) | OrderMind with SCU only · Simulation Moderate | success rate (%): 81.0 -> 87.7 | Helped |
| Add spatial priors order labeling (SPOL) to SCU (ablation) | OrderMind with SCU only · Simulation Moderate | success rate (%): 81.0 -> 91.4 | Helped |
| Add SPOL to TPS (ablation) | OrderMind with TPS only · Simulation Moderate | success rate (%): 80.5 -> 90.5 | Helped |
| Add SCU, TPS, and SPOL together (ablation) | OrderMind baseline without SCU, TPS, or SPOL · Simulation Moderate | success rate (%): 76.1 -> 95.3 | Helped |
| Set the order loss weight to 3 instead of 5 (ablation) | OrderMind · Simulation Moderate | success rate (%): 95.3 -> 89.6 | Hurt |
| Set the order loss weight to 7 instead of 5 (ablation) | OrderMind · Simulation Moderate | success rate (%): 95.3 -> 91.3 | Hurt |
| Set the order loss weight to 10 instead of 5 (ablation) | OrderMind · Simulation Moderate | success rate (%): 95.3 -> 92.2 | Hurt |
Surprising findings
- Even with privileged ground-truth object information, the best VLM reaches only 92.5% success in easy and 78.5% in hard settings.
- Under 70% simulated VLM label noise, performance drops substantially, degenerating toward a random policy.
Limitations
- Assumes a stable scene during execution, limiting adaptation to dynamic environments.
- Relies on accurate 3D attribute estimation, which degrades under severe occlusion.
- Performance degrades substantially under high VLM label noise (70%).
Experimental setup
Datasets: YCB object set (PyBullet simulation) real-world cluttered scene images
Benchmarks: Manipulation Ordering Benchmark (simulation easy/moderate/hard) real-world closed-loop tests generalization tray setting
Metrics: success rate residual count object disturbance FPS Levenshtein Distance mAP50
Baselines: GPT-4o Claude-3.7-Sonnet Gemini-2.5 Gemma-3 Qwen2.5-VL InternVL3 YOLOv11-seg+SPH YOLOv11-det+SPH UniDet3D+Confidence UniDet3D+Distance
Builds on: PointNet UniDet3D YOLOv11
Closest papers by meaning
- OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints (CVPR 2025 · similarity 0.86)
- Localizing, Structuring, and Rendering: Bridging 3D and 2D Vision-Language-Action Models for Robotic Manipulation (CVPR 2026 · similarity 0.86)
- RoboGround: Robotic Manipulation with Grounded Vision-Language Priors (CVPR 2025 · similarity 0.86)
- SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation (NeurIPS 2025 · similarity 0.86)
- AffordDP: Generalizable Diffusion Policy with Transferable Affordance (CVPR 2025 · similarity 0.85)
- SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation (ICML 2025 · similarity 0.85)
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