Learning Spatial-Aware Manipulation Ordering

2025-10-28

AI Summary

In cluttered environments, spatial dependencies among objects often lead to collisions or occlusions; existing methods frequently neglect these relationships, limiting operational flexibility and generalization. To address this, we propose OrderMind—a novel framework that unifies spatial context encoding and temporal priority modeling within an end-to-end operational sequencing planner. Specifically, we construct an object–manipulator interaction graph via k-nearest neighbors and leverage vision-language models to generate high-quality spatial priors for sequence supervision. Our approach jointly models intra-object structural constraints and inter-object–actuator spatial relationships. Evaluated on a benchmark of 160K samples, OrderMind consistently outperforms state-of-the-art methods in both simulation and real-world settings, achieving significant improvements in real-time inference, collision-free success rate, and kinematic reachability. The framework establishes a scalable, sequential decision-making paradigm for robust manipulation in complex, unstructured environments.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Intelligent Robots: Manipulation
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Manipulation in cluttered environments is challenging due to spatial dependencies among objects, where an improper manipulation order can cause collisions or blocked access. Existing approaches often overlook these spatial relationships, limiting their flexibility and scalability. To address these limitations, we propose OrderMind, a unified spatial-aware manipulation ordering framework that directly learns object manipulation priorities based on spatial context. Our architecture integrates a spatial context encoder with a temporal priority structuring module. We construct a spatial graph using k-Nearest Neighbors to aggregate geometric information from the local layout and encode both object-object and object-manipulator interactions to support accurate manipulation ordering in real-time. To generate physically and semantically plausible supervision signals, we introduce a spatial prior labeling method that guides a vision-language model to produce reasonable manipulation orders for distillation. We evaluate OrderMind on our Manipulation Ordering Benchmark, comprising 163,222 samples of varying difficulty. Extensive experiments in both simulation and real-world environments demonstrate that our method significantly outperforms prior approaches in effectiveness and efficiency, enabling robust manipulation in cluttered scenes.

Problem

• Learning object manipulation priorities from spatial context
• Addressing spatial dependencies to prevent collisions in cluttered environments
• Generating physically plausible manipulation orders through vision-language distillation

Innovation

• Learns object manipulation priorities using spatial context
• Integrates spatial encoder with temporal priority structuring
• Uses spatial graph and vision-language model for supervision