GraspTwin: Zero-Shot Task-Oriented Grasp Optimization via a Digital Twin

arXiv:2609.30543 · cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/VT-Collab/GraspTwin

Terminology

• Digital Twin — 36× in this paper · explained in 4 episodes
• Foundation Model — 12× in this paper · explained in 3 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Affordance — 8× in this paper · explained in 1 episode
• Bayesian Optimization — 8× in this paper · explained in 3 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Grounding — 6× in this paper · explained in 3 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Local Optimization — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models
• Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations
• GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
• Human Universal Grasping
• Learning Dexterous Grasping from Sparse Taxonomy Guidance
• Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement
• SegGrasp: Zero-Shot Task-Oriented Grasping via Semantic and Geometric Guided Segmentation
• Automated Creation of Digital Cousins for Robust Policy Learning
• Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
• Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
• SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning

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