GRIM: Task-Oriented Grasping with Conditioning on Generative Examples

2025-06-18

AI Summary

Task-oriented grasping (TOG) faces challenges in semantic understanding, functional attribute modeling, and joint reasoning under task constraints. To address these, we propose GRIM—a training-free framework that introduces generative, example-conditioned grasp pose recalibration for the first time. Methodologically: (1) coarse alignment is achieved by fusing geometric cues with DINO visual features reduced via PCA; (2) fine-grained adaptation is realized through a task-compatibility-driven pose refinement mechanism, leveraging iterative generative sampling and feature-space transfer optimization. This training-free paradigm requires only a few demonstration examples yet achieves significant improvements in grasp success rate and functional appropriateness across diverse objects and tasks. GRIM establishes a lightweight, highly generalizable, and semantically interpretable paradigm for TOG—eliminating the need for large-scale training data or task-specific fine-tuning while preserving physical feasibility and functional intent.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Task-Oriented Grasping (TOG) presents a significant challenge, requiring a nuanced understanding of task semantics, object affordances, and the functional constraints dictating how an object should be grasped for a specific task. To address these challenges, we introduce GRIM (Grasp Re-alignment via Iterative Matching), a novel training-free framework for task-oriented grasping. Initially, a coarse alignment strategy is developed using a combination of geometric cues and principal component analysis (PCA)-reduced DINO features for similarity scoring. Subsequently, the full grasp pose associated with the retrieved memory instance is transferred to the aligned scene object and further refined against a set of task-agnostic, geometrically stable grasps generated for the scene object, prioritizing task compatibility. In contrast to existing learning-based methods, GRIM demonstrates strong generalization capabilities, achieving robust performance with only a small number of conditioning examples.

Problem

• Addresses task-oriented grasping with semantic understanding
• Develops training-free framework using geometric and PCA-reduced features
• Enhances generalization with few conditioning examples

Innovation

• Training-free framework for task-oriented grasping
• Geometric cues and PCA-reduced DINO features
• Task-agnostic geometrically stable grasps refinement