DHAGrasp: Synthesizing Affordance-Aware Dual-Hand Grasps with Text Instructions
DHAGrasp synthesizes dual-hand grasps guided by text, improving grasp quality on unseen objects.
Key Findings
Methodology
The paper introduces SymOpt, a pipeline that leverages existing single-hand datasets to generate large-scale dual-hand grasp datasets. Based on this, DHAGrasp, a text-guided dual-hand grasp generator, is proposed, using a two-stage design to effectively learn from a small set of segmented training objects while scaling to a larger pool of unsegmented data.
Key Results
- In experiments, DHAGrasp significantly outperformed baseline methods in grasp success rates on unseen objects, achieving up to 99.76% under varying friction coefficients.
- On the DualHands-Full dataset, DHAGrasp's generated grasps showed superior physical plausibility compared to BimanGrasp.
- Ablation studies confirmed the effectiveness of the dual-hand contact representation and text-guided module, enhancing semantic consistency of grasps.
Significance
This research addresses the limitations of existing datasets in semantic annotation by introducing semantic awareness in dual-hand grasping, advancing the application of robots in complex grasping tasks. The generated grasp dataset provides rich training resources for future research.
Technical Contribution
Technical contributions include the SymOpt pipeline for generating large-scale dual-hand grasp datasets, a dual-hand contact representation enhancing semantic awareness, and the development of DHAGrasp, a text-guided dual-hand grasp generator that significantly improves grasp diversity and consistency.
Novelty
DHAGrasp is the first to integrate text guidance into dual-hand grasp generation, combining semantic awareness and a two-stage design to significantly enhance grasp quality on unseen objects.
Limitations
- The approach relies on existing single-hand grasp datasets, which may still have limitations in semantic annotation.
- The grasp generation may be less effective for completely asymmetric objects.
Future Work
Future work could explore more complex semantic annotation methods to enhance semantic consistency in grasp generation and expand to more diverse object categories.
AI Executive Summary
Generating dual-hand grasps that respect object semantics is crucial for robust hand-object interaction in robotics. However, existing datasets primarily focus on single-hand grasps and lack rich semantic annotations. To address this, researchers have developed the SymOpt pipeline, which leverages existing single-hand datasets and object symmetries to generate large-scale dual-hand grasp datasets. Building on this, DHAGrasp, a text-guided dual-hand grasp generator, has been developed to produce diverse and semantically consistent grasps for unseen objects.
DHAGrasp employs an innovative dual-hand contact representation and a two-stage design, enabling effective learning from a small set of segmented training objects while scaling to a larger pool of unsegmented data. Experimental results demonstrate that this method outperforms strong baseline methods in both grasp quality and generalization to unseen objects.
This research not only opens new possibilities for the application of robots in complex grasping tasks but also provides rich training resources for future research. However, there is still room for improvement in the semantic annotation of generated dual-hand grasps, and future work could explore more complex semantic annotation methods and expand to more diverse object categories.
Deep Analysis
Background
In the field of robotics, generating grasps that respect object semantics is crucial for robust hand-object interaction. However, existing grasp datasets primarily focus on single-hand interactions and lack rich semantic annotations, limiting research and application in dual-hand grasping. To overcome this challenge, researchers have developed the SymOpt pipeline, which leverages existing single-hand datasets to generate large-scale dual-hand grasp datasets.
Core Problem
The core problem is how to generate dual-hand grasps that respect object semantics. Existing datasets have limited semantic annotations, making it difficult to support complex dual-hand grasping tasks. Additionally, the generated grasps need to be physically plausible and generalize to unseen objects.
Innovation
The innovations of this paper include the SymOpt pipeline and the DHAGrasp generator. SymOpt generates large-scale dual-hand grasp datasets by mirroring existing single-hand grasps. DHAGrasp uses text guidance to generate dual-hand grasps, employing a two-stage design to enhance semantic awareness.
Methodology
- �� SymOpt pipeline: Generates dual-hand grasp datasets using single-hand grasp datasets.
- �� DHAGrasp generator: Uses a two-stage design and text guidance to generate dual-hand grasps.
- �� Dual-hand contact representation: Enhances semantic awareness, improving grasp diversity and consistency.
Experiments
The experimental design includes evaluations on the DualHands-Full and DualHands-Sem datasets, comparing the grasp quality and semantic consistency of baseline methods and DHAGrasp. Different friction coefficients are used to validate the physical plausibility of the grasps, and ablation studies assess the contributions of each component.
Results
Experimental results show that DHAGrasp significantly outperformed baseline methods in grasp success rates on unseen objects, achieving up to 99.76% under high friction coefficients. Additionally, ablation studies confirmed the effectiveness of the dual-hand contact representation and text-guided module, enhancing semantic consistency of grasps.
Applications
Application scenarios for DHAGrasp include complex grasping tasks in industrial automation and home service robots. The method can generate diverse and semantically consistent grasps, enhancing the operational capabilities of robots.
Limitations & Outlook
Despite significant progress in generating dual-hand grasps, DHAGrasp relies on existing single-hand grasp datasets, which may still have limitations in semantic annotation. Additionally, the grasp generation may be less effective for completely asymmetric objects.
Plain Language Accessible to non-experts
Imagine you're in a kitchen trying to lift a large pot with both hands. DHAGrasp is like a smart assistant that understands how you want to grab the pot and helps you find the best way to do it. By analyzing the pot's shape and the instructions you give, it generates a dual-hand grasp plan. Just like you adjust your grip based on the pot's size and shape, DHAGrasp adjusts its grasp strategy based on the object's different features.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to grab a virtual object with both hands. DHAGrasp is like a super helper in the game, finding the best way for you to grab it. You just tell it what you want to do, and it uses the object's shape and your instructions to create a perfect grasp plan. Just like in a game where you adjust your grip for different objects, DHAGrasp changes its strategy based on the object's features. Isn't that cool?
Glossary
SymOpt
A pipeline that generates dual-hand grasp datasets using single-hand grasp data.
Used to generate large-scale dual-hand grasp datasets.
DHAGrasp
A generator that creates dual-hand grasps guided by text instructions.
Used to generate semantically consistent dual-hand grasps.
DualHands-Full
A large-scale dual-hand grasp dataset with various object categories.
Used for training and evaluating grasp generators.
Text2Dir
A module that predicts grasp directions based on object shape and text embedding.
Used to generate semantic directions for grasps.
Dir2Grasp
A module that generates dual-hand grasps based on predicted grasp directions.
Used to generate final dual-hand grasps.
Open Questions Unanswered questions from this research
- 1 How to generate high-quality dual-hand grasps for completely asymmetric objects? Current methods perform well on symmetric objects but need improvement for asymmetric ones.
- 2 How to automate the generation of more complex semantic annotations? Current methods rely on manual annotation, limiting dataset scale and diversity.
Applications
Immediate Applications
Industrial Automation
Robots can use DHAGrasp-generated grasps on production lines, improving operational efficiency and safety.
Long-term Vision
Home Service Robots
In the future, home service robots could use DHAGrasp-generated grasps to perform more complex household tasks.
Abstract
Learning to generate dual-hand grasps that respect object semantics is essential for robust hand-object interaction but remains largely underexplored due to dataset scarcity. Existing grasp datasets predominantly focus on single-hand interactions and contain only limited semantic part annotations. To address these challenges, we introduce a pipeline, SymOpt, that constructs a large-scale dual-hand grasp dataset by leveraging existing single-hand datasets and exploiting object and hand symmetries. Building on this, we propose a text-guided dual-hand grasp generator, DHAGrasp, that synthesizes Dual-Hand Affordance-aware Grasps for unseen objects. Our approach incorporates a novel dual-hand affordance representation and follows a two-stage design, which enables effective learning from a small set of segmented training objects while scaling to a much larger pool of unsegmented data. Extensive experiments demonstrate that our method produces diverse and semantically consistent grasps, outperforming strong baselines in both grasp quality and generalization to unseen objects. The project page is at https://quanzhou-li.github.io/DHAGrasp/.