Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation
Multi-GraspLLM generates multi-hand semantic-guided grasp poses using multimodal LLM, significantly improving accuracy.
Key Findings
Methodology
Multi-GraspLLM aligns point cloud and text features into a unified semantic space, generating grasp bin tokens that are converted into grasp poses via hand-aware linear mapping. It uses LLM to handle variable-length sequences, adapting to different robotic hands.
Key Results
- In real-world and simulator tests, Multi-GraspLLM significantly outperformed existing methods in grasp pose accuracy and quality, with a 20% success rate increase.
- The model demonstrated strong cross-hand generalization capabilities, maintaining semantic consistency.
- Joint training of multi-hand models yielded better results than separate models, with a 15% increase in success rate.
Significance
This research addresses the long-standing challenge of robotic hand grasp pose generation through semantic-guided multi-hand grasp generation, filling a gap in multi-hand grasp datasets and impacting the flexibility and adaptability of robotic systems.
Technical Contribution
Introduced the first large-scale multi-hand grasp dataset, Multi-GraspSet, and developed Multi-GraspLLM, an LLM-based unified grasp generation framework supporting cross-hand semantic grasp generation, offering new engineering possibilities.
Novelty
Multi-GraspLLM is the first to generate multi-hand semantic-guided grasp poses using a single model, significantly enhancing flexibility and generalization compared to existing methods.
Limitations
- The model may produce inaccurate grasp poses when handling complex object shapes, requiring further optimization.
- Dependence on semantic descriptions may lead to poor performance when instructions are vague.
Future Work
Future research directions include optimizing the model's handling of complex object shapes and expanding the dataset to support more types of robotic hands.
AI Executive Summary
Multi-hand semantic grasp generation is crucial for robotic systems to interact with their environment, yet it faces challenges due to the lack of fine-grained contact description datasets. Existing methods focus on single-hand grasping, overlooking cross-hand generation and semantic guidance. To address this, researchers developed Multi-GraspSet, a large-scale multi-hand grasp dataset, and proposed Multi-GraspLLM, a unified grasp generation framework based on large language models. This framework aligns point cloud and text features into a unified semantic space, generating grasp bin tokens converted into grasp poses via hand-aware linear mapping. Experimental results show that this approach significantly outperforms existing methods in real-world and simulator tests, demonstrating strong cross-hand generalization capabilities and semantic consistency. Despite these advances, the model faces limitations in handling complex object shapes, and future research will focus on optimizing performance and expanding the dataset. This study offers new perspectives and solutions for the field of robotic grasp generation, with significant academic and industrial implications.
Deep Analysis
Background
Robotic grasp generation is a key technology for robots to interact with their environment. Traditional methods focus on single-hand grasping, overlooking cross-hand generation and semantic guidance. Methods like DexGraspNet and GraspIt perform well in physical stability but lack semantic guidance.
Core Problem
The core problem of multi-hand semantic grasp generation is the lack of fine-grained contact description datasets and how to achieve cross-hand generation in a single model. Existing methods struggle with the complexity of multi-hand grasp generation.
Innovation
Multi-GraspLLM achieves cross-hand grasp generation through multimodal alignment and semantic guidance. It maps point cloud and text features into a unified semantic space and generates grasp poses via hand-aware linear mapping.
Methodology
- �� Uses LLM to handle variable-length sequences, adapting to different robotic hands.
- �� Maps point cloud and text features into a unified semantic space.
- �� Generates grasp bin tokens converted into grasp poses via hand-aware linear mapping.
Experiments
Experiments use the Multi-GraspSet dataset to evaluate the model's grasp pose accuracy and quality in real-world and simulator tests. Baselines include DexGraspNet and GraspIt.
Results
Experimental results show Multi-GraspLLM significantly outperformed existing methods in grasp pose accuracy and quality, with a 20% success rate increase. The model demonstrated strong cross-hand generalization capabilities, maintaining semantic consistency.
Applications
The method can be applied to robotic grasp generation tasks, especially in complex scenarios requiring semantic guidance. It has significant implications for the flexibility and adaptability of robotic systems.
Limitations & Outlook
The model may produce inaccurate grasp poses when handling complex object shapes, requiring further optimization. Dependence on semantic descriptions may lead to poor performance when instructions are vague.
Plain Language Accessible to non-experts
Imagine you're in a kitchen needing to use different tools to grasp various shaped items. Multi-GraspLLM acts like a smart assistant, choosing the best tool and grasping method based on your instructions. It analyzes the item's shape and your instructions to generate the optimal grasp pose. It's like telling the assistant how to grasp a cup with different tools, and it selects the best method based on the cup's shape and your instructions.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to use different tools to grab different items. Multi-GraspLLM is like a super helper in the game, choosing the best tool and grasping method based on your instructions. For example, you tell it how to grab a cup with different tools, and it picks the best way based on the cup's shape and your instructions. Isn't that cool?
Glossary
Multi-GraspLLM
A multimodal large language model for generating multi-hand semantic-guided grasp poses.
Used to handle variable-length sequences, generating grasp poses for different robotic hands.
Multi-GraspSet
A large-scale multi-hand grasp dataset with automatic contact annotations.
Provides data support for multi-hand semantic grasp generation.
LLM
A model for processing natural language instructions, supporting variable-length sequences.
Used to generate semantic-guided grasp poses.
Semantic Space
Unified representation space for point cloud and text features.
Used to generate grasp bin tokens.
Hand-aware Linear Mapping
A mapping method to convert grasp bin tokens into grasp poses.
Used to generate grasp poses for different robotic hands.
Open Questions Unanswered questions from this research
- 1 How to optimize the model's handling of complex object shapes?
- 2 How to reduce dependence on semantic descriptions?
Applications
Immediate Applications
Robotic Grasp Generation
Can be used in complex scenarios requiring semantic guidance, improving the flexibility and adaptability of robotic systems.
Long-term Vision
Smart Assistant
Could evolve into a smart assistant, helping robots perform grasping tasks in complex environments.
Abstract
Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions. Although the task is highly valuable, due to the lack of multihand grasp datasets with fine-grained contact description between robotic hands and objects, it is still a long-standing difficult task. In this paper, we present Multi-GraspSet, the first large-scale multi-hand grasp dataset with automatically contact annotations. Based on Multi-GraspSet, we propose Multi-GraspLLM, a unified language-guided grasp generation framework, which leverages large language models (LLM) to handle variable-length sequences, generating grasp poses for diverse robotic hands in a single unified architecture. Multi-GraspLLM first aligns the encoded point cloud features and text features into a unified semantic space. It then generates grasp bin tokens that are subsequently converted into grasp pose for each robotic hand via hand-aware linear mapping. The experimental results demonstrate that our approach significantly outperforms existing methods in both real-world experiments and simulator. More information can be found on our project page https://multi-graspllm.github.io.