Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation

• URL: http://arxiv.org/abs/2512.21065v1
• Date: Wed, 24 Dec 2025 09:16:42 GMT
• Title: Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation
• Authors: Zebin Jiang, Tianle Jin, Xiangtong Yao, Alois Knoll, Hu Cao,
• Abstract summary: We propose Language-Guided Grasp Detection (LGGD) with a coarse-to-fine learning paradigm for robotic manipulation.<n>This design enables fine-grained visual-semantic alignment and improves the feasibility of the predicted grasps with respect to task instructions.<n>Experiments on the OCID-VLG and Grasp-Anything++ datasets show that LGGD surpasses existing language-guided grasping methods.
• Score: 31.386822229629455
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Grasping is one of the most fundamental challenging capabilities in robotic manipulation, especially in unstructured, cluttered, and semantically diverse environments. Recent researches have increasingly explored language-guided manipulation, where robots not only perceive the scene but also interpret task-relevant natural language instructions. However, existing language-conditioned grasping methods typically rely on shallow fusion strategies, leading to limited semantic grounding and weak alignment between linguistic intent and visual grasp reasoning.In this work, we propose Language-Guided Grasp Detection (LGGD) with a coarse-to-fine learning paradigm for robotic manipulation. LGGD leverages CLIP-based visual and textual embeddings within a hierarchical cross-modal fusion pipeline, progressively injecting linguistic cues into the visual feature reconstruction process. This design enables fine-grained visual-semantic alignment and improves the feasibility of the predicted grasps with respect to task instructions. In addition, we introduce a language-conditioned dynamic convolution head (LDCH) that mixes multiple convolution experts based on sentence-level features, enabling instruction-adaptive coarse mask and grasp predictions. A final refinement module further enhances grasp consistency and robustness in complex scenes.Experiments on the OCID-VLG and Grasp-Anything++ datasets show that LGGD surpasses existing language-guided grasping methods, exhibiting strong generalization to unseen objects and diverse language queries. Moreover, deployment on a real robotic platform demonstrates the practical effectiveness of our approach in executing accurate, instruction-conditioned grasp actions. The code will be released publicly upon acceptance.

Related papers

• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity [23.77040677368575]
  We introduce a novel robotic manipulation framework that can accomplish tasks specified by potentially ambiguous natural language.<n>This framework employs a Vision-Language Model (VLM) to interpret abstract concepts in natural language instructions.<n>We show that our approach excels across challenging manipulation tasks involving language ambiguity, contact-rich manipulation, and multi-object interactions.
  arXiv  Detail & Related papers  (2025-06-19T23:42:03Z)
• Interpretable Robotic Manipulation from Language [11.207620790833271]
  We introduce an explainable behavior cloning agent, named Ex-PERACT, specifically designed for manipulation tasks. At the top level, the model is tasked with learning a discrete skill code, while at the bottom level, the policy network translates the problem into a voxelized grid and maps the discretized actions to voxel grids. We evaluate our method across eight challenging manipulation tasks utilizing the RLBench benchmark, demonstrating that Ex-PERACT not only achieves competitive policy performance but also effectively bridges the gap between human instructions and machine execution in complex environments.
  arXiv  Detail & Related papers  (2024-05-27T11:02:21Z)
• Learning with Language-Guided State Abstractions [58.199148890064826]
  Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations. Our method, LGA, uses a combination of natural language supervision and background knowledge from language models to automatically build state representations tailored to unseen tasks. Experiments on simulated robotic tasks show that LGA yields state abstractions similar to those designed by humans, but in a fraction of the time.
  arXiv  Detail & Related papers  (2024-02-28T23:57:04Z)
• Object-Centric Instruction Augmentation for Robotic Manipulation [29.491990994901666]
  We introduce the textitObject-Centric Instruction Augmentation (OCI) framework to augment highly semantic and information-dense language instruction with position cues. We utilize a Multi-modal Large Language Model (MLLM) to weave knowledge of object locations into natural language instruction. We demonstrate that robotic manipulator imitation policies trained with our enhanced instructions outperform those relying solely on traditional language instructions.
  arXiv  Detail & Related papers  (2024-01-05T13:54:45Z)
• SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects [9.500480417077272]
  We propose a novel framework that bridges semantic and actionable parts of articulated objects to achieve generalizable manipulation under natural language instructions. A part-grounding module maps the semantic parts into so-called Generalizable Actionable Parts (GAParts), which inherently carry information about part motion. An interactive feedback module is incorporated to respond to failures, which closes the loop and increases the robustness of the overall framework.
  arXiv  Detail & Related papers  (2023-12-03T07:22:42Z)
• LanGWM: Language Grounded World Model [24.86620763902546]
  We focus on learning language-grounded visual features to enhance the world model learning. Our proposed technique of explicit language-grounded visual representation learning has the potential to improve models for human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-29T12:41:55Z)
• Language-Driven Representation Learning for Robotics [115.93273609767145]
  Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. We introduce a framework for language-driven representation learning from human videos and captions. We find that Voltron's language-driven learning outperform the prior-of-the-art, especially on targeted problems requiring higher-level control.
  arXiv  Detail & Related papers  (2023-02-24T17:29:31Z)
• PADL: Language-Directed Physics-Based Character Control [66.517142635815]
  We present PADL, which allows users to issue natural language commands for specifying high-level tasks and low-level skills that a character should perform. We show that our framework can be applied to effectively direct a simulated humanoid character to perform a diverse array of complex motor skills.
  arXiv  Detail & Related papers  (2023-01-31T18:59:22Z)
• Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
  We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings. We demonstrate that this framework enables effective generalization across different environments. For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
  arXiv  Detail & Related papers  (2022-02-03T18:55:52Z)
• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision [110.66085917826648]
  We develop a technique that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images. "vokenization" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks.
  arXiv  Detail & Related papers  (2020-10-14T02:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.