# ORACLE-Grasp: Zero-Shot Affordance-Aligned Robotic Grasping using Large Multimodal Models

arXiv: https://arxiv.org/abs/2505.08417 · HTML: https://mlsanity.com/paper/2505.08417/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 34 of its 47 references that are in the map.
- First posted on arXiv 13 May 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 30 of 34)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Simple Open-Vocabulary Object Detection with Vision Transformers](https://mlsanity.com/paper/2205.06230/) (2022)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning](https://mlsanity.com/paper/1803.09956/) (2018)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks](https://mlsanity.com/paper/2408.01319/) (2024)
- [GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping](https://mlsanity.com/paper/2307.13204/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Reasoning Grasping via Multimodal Large Language Model](https://mlsanity.com/paper/2402.06798/) (2024)
- [FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models](https://mlsanity.com/paper/2404.10399/) (2024)
- [Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement](https://mlsanity.com/paper/2310.05239/) (2023)
- [Task-Oriented Grasp Prediction with Visual-Language Inputs](https://mlsanity.com/paper/2302.14355/) (2023)
- [ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition](https://mlsanity.com/paper/2403.18062/) (2024)
- [RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models](https://mlsanity.com/paper/2310.10221/) (2023)

## Abstract (arXiv)

Grasping unknown objects in unstructured environments is a critical challenge for service robots, which must operate in dynamic, real-world settings such as homes, hospitals, and warehouses. Success in these environments requires both semantic understanding and spatial reasoning. Traditional methods often rely on dense training datasets or detailed geometric modeling, which demand extensive data collection and do not generalize well to novel objects or affordances. We present ORACLE-Grasp, a zero-shot framework that leverages Large Multimodal Models (LMMs) as semantic oracles to guide affordance-aligned grasp selection, without requiring task-specific training or manual input. The system reformulates grasp prediction as a structured, iterative decision process, using a dual-prompt tool-calling strategy: the first prompt extracts high-level object semantics, while the second identifies graspable regions aligned with the object's function. To address the spatial limitations of LMMs, ORACLE-Grasp discretizes the image into candidate regions and reasons over them to produce human-like and context-sensitive grasp suggestions. A depth-based refinement step improves grasp reliability when available, and an early stopping mechanism enhances computational efficiency. We evaluate ORACLE-Grasp on a diverse set of RGB and RGB-D images featuring both everyday and AI-generated objects. The results show that our method produces physically feasible and semantically appropriate grasps that align closely with human annotations, achieving high success rates in real-world pick-up tasks. Our findings highlight the potential of LMMs for enabling flexible and generalizable grasping strategies in autonomous service robots, eliminating the need for object-specific models or extensive training.