BOP-ASK: Object-Interaction Reasoning for Vision-Language Models

TL;DR

BOP-ASK is a large-scale dataset designed to improve object interaction reasoning in vision-language models, emphasizing fine-grained spatial understanding and physical reasoning.

Contribution

The paper introduces BOP-ASK, a novel dataset with detailed annotations for object interaction reasoning, enabling better training and benchmarking of VLMs.

Findings

Models trained on BOP-ASK outperform baselines.

Trained models show emergent capabilities like pose estimation and trajectory planning.

BOP-ASK enables testing of generalization with out-of-distribution data.

Abstract

Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interactions. Current benchmarks test high level relationships ('left of,' 'behind', etc.) but ignore fine-grained spatial understanding needed for real world applications: precise 3D localization, physical compatibility between objects, object affordances and multi step spatial planning. In this work, we present BOP-ASK, a novel large scale dataset for object interaction reasoning for both training and benchmarking. Our data generation pipeline leverages 6D object poses from the Benchmark for Object Pose Estimation (BOP) datasets from which we derive fine grained annotations such as grasp poses, referred object poses, path planning trajectories, relative spatial and depth relationships, and object-to-object relationships. BOP-ASK comprises over 150k images and 33M question answer pairs spanning six tasks (four novel), providing a rich resource for training and evaluating VLMs. We evaluate proprietary and open sourced VLMs, and conduct human evaluations on BOP-ASK-core, a contributed test benchmark. We also release BOP-ASK-lab, an out-of-distribution benchmark with images not sourced from BOP, enabling testing of generalization. Our experiments demonstrate that models trained on BOP-ASK outperform baselines and exhibit emergent capabilities such as precise object and grasp pose estimation, trajectory planning, and fine-grained object-centric spatial reasoning in cluttered environments.