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Grasp as You Say: Language-guided Dexterous Grasp Generation
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Introduces the DexGYS task with the DexGYSNet language-guided dexterous grasp dataset (50k grasps on 1,800 objects) and the DexGYSGrasp two-component diffusion framework that decouples intention/diversity from penetration-aware quality refinement.
Problem
Generating dexterous grasps that follow natural-language human commands while jointly achieving intention alignment, high grasp quality, and diversity; prior datasets lack language-annotated dexterous grasps and penetration losses hinder learning of the grasp distribution.
Approach
Decompose learning into two progressive objectives: a conditional diffusion component trained without penetration loss to capture language-aligned, diverse grasp distributions, followed by a regression-based quality component that refines coarse grasps with penetration loss while preserving intention.
Key results
- On DexGYSNet, the proposed method reaches FID 6.538 and P-FID 5.595 versus 20.44 and 7.932 for the best baseline SceneDiffuser.
- Success rate of 63.31% in Isaac Gym, compared with 62.24% for SceneDiffuser and 29.12% for GraspCVAE.
- Real-world Allegro hand experiments show success on 10 trials per object, ranging from 3/10 (trigger sprayer, frying pan) to 9/10 (power drill).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add penetration loss to IDGC (ablation) | DexGYSGrasp on DexGYSNet · DexGYSNet | CD: 1.276 -> 2.980 | Hurt |
| increase IDGC penetration-loss weight from 50 to 500 (ablation) | DexGYSGrasp on DexGYSNet · DexGYSNet | Pen.: 0.271 -> 0.037 | Helped |
| increase IDGC penetration-loss weight from 0 to 500 (ablation) | DexGYSGrasp on DexGYSNet · DexGYSNet | δt: 5.710 -> 0.547 | Hurt |
| increase penetration-loss weight from 0 to 100 after several training epochs in a single component (ablation) | DexGYSGrasp on DexGYSNet · DexGYSNet | CD: 1.276 -> 3.181 | Hurt |
| add test-time adaptation to IDGC without penetration loss (ablation) | DexGYSGrasp on DexGYSNet · DexGYSNet | CD: 1.276 -> 20.09 | Hurt |
| add QGC after IDGC trained without penetration loss (ablation) | DexGYSGrasp on DexGYSNet · DexGYSNet | CD: 1.276 -> 1.198 | Helped |
| remove object penetration loss (ablation) | GraspCVAE on DexGYSNet · DexGYSNet | CD: 3.138 -> 2.638 | Helped |
| remove object penetration loss (ablation) | SceneDiffuser on DexGYSNet · DexGYSNet | CD: 1.679 -> 1.511 | Helped |
| add QGC after GraspCVAE without penetration loss (ablation) | GraspCVAE on DexGYSNet · DexGYSNet | Q₁: 0.005 -> 0.074 | Helped |
| add QGC after SceneDiffuser without penetration loss (ablation) | SceneDiffuser on DexGYSNet · DexGYSNet | Q₁: 0.019 -> 0.082 | Helped |
| use three-step HOIR instead of optimizing all losses in one stage (ablation) | DexGYSNet construction · DexGYSNet HOIR | Con.: 0.075 -> 0.015 | Helped |
| fix root translation in HOIR step 3 (ablation) | DexGYSNet construction · DexGYSNet HOIR | Con.: 0.051 -> 0.015 | Helped |
Surprising findings
- Penetration loss hinders learning of the language-conditioned grasp distribution, reducing both intention alignment and diversity.
- Test-time adaptation for quality refinement improves grasp quality but yields very poor intention consistency.
Limitations
- Real-world grasp success is moderate (3/10 to 9/10 across objects) and not guaranteed.
- Real-world pipeline relies on cascaded off-the-shelf perception and completion models rather than the framework alone.
- Quality and intention trade off in the HOIR dataset construction, as noted by the authors.
Experimental setup
Datasets: OakInk DexGYSNet
Benchmarks: DexGYSNet
Metrics: FID P-FID Chamfer distance contact distance success rate Q1 max penetration depth translation/rotation/joint std diversity
Baselines: GraspCVAE GraspTTA SceneDiffuser DGTR
Builds on: DDPM PointNet++ GraspTTA OakInk DexGraspNet
Closest papers by meaning
- Language-driven Grasp Detection (CVPR 2024 · similarity 0.89)
- Scaffolding Dexterous Manipulation with Vision-Language Models (NeurIPS 2025 · similarity 0.88)
- ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR 2025 · similarity 0.87)
- DexFuncGrasp: A Robotic Dexterous Functional Grasp Dataset Constructed from a Cost-Effective Real-Simulation Annotation System (AAAI 2024 · similarity 0.87)
- RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation (CVPR 2026 · similarity 0.87)
- DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness (CVPR 2025 · similarity 0.86)
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