Map Space Belief Prediction for Manipulation-Enhanced Mapping

2025-02-28

AI Summary

To address occlusion, pose ambiguity, and category uncertainty in object recognition within cluttered retail shelves, this paper proposes a POMDP framework grounded in metric-semantic occupancy grid maps, enabling joint inference of object location, category, shape, occlusion status, and manipulability. We introduce a novel map-space belief update paradigm and present Calibrated Neural Accelerated Belief Updating (CNABU)—the first end-to-end, general-purpose, and uncertainty-calibrated map belief propagation method. Our approach integrates neural belief modeling, calibrated probabilistic prediction, POMDP-based planning, and physics-aware manipulation modeling. Extensive simulation experiments demonstrate significant improvements in map completeness and object localization accuracy. Moreover, the method successfully transfers to real-world cluttered shelf scenes under zero-shot conditions. This work establishes a new paradigm for robust object recognition in open, unstructured environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has to efficiently identify all objects in a cluttered shelf. Although Partially Observable Markov Decision Processes~(POMDPs) are standard for decision-making under uncertainty, representing unstructured interactive worlds remains challenging in this formalism. To tackle this, we define a POMDP whose belief is summarized by a metric-semantic grid map and propose a novel framework that uses neural networks to perform map-space belief updates to reason efficiently and simultaneously about object geometries, locations, categories, occlusions, and manipulation physics. Further, to enable accurate information gain analysis, the learned belief updates should maintain calibrated estimates of uncertainty. Therefore, we propose Calibrated Neural-Accelerated Belief Updates (CNABUs) to learn a belief propagation model that generalizes to novel scenarios and provides confidence-calibrated predictions for unknown areas. Our experiments show that our novel POMDP planner improves map completeness and accuracy over existing methods in challenging simulations and successfully transfers to real-world cluttered shelves in zero-shot fashion.

Problem

• Efficiently identify objects in cluttered shelves using manipulation-enhanced semantic mapping.
• Address challenges in representing unstructured interactive worlds with POMDPs.
• Develop calibrated neural-accelerated belief updates for accurate uncertainty estimation.

Innovation

• POMDP with metric-semantic grid map belief
• Neural networks for map-space belief updates
• Calibrated Neural-Accelerated Belief Updates (CNABUs)