# SECOND-Grasp: Semantic Contact-guided Dexterous Grasping

arXiv: https://arxiv.org/abs/2605.13117 · HTML: https://mlsanity.com/paper/2605.13117/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 48 of its 58 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 30 of 48)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy](https://mlsanity.com/paper/2303.00938/) (2023)
- [UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning](https://mlsanity.com/paper/2304.00464/) (2023)
- [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://mlsanity.com/paper/2211.11744/) (2022)
- [3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding](https://mlsanity.com/paper/2103.16397/) (2021)
- [DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping](https://mlsanity.com/paper/2502.20900/) (2025)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [Grounding 3D Object Affordance from 2D Interactions in Images](https://mlsanity.com/paper/2303.10437/) (2023)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [AffordanceLLM: Grounding Affordance from Vision Language Models](https://mlsanity.com/paper/2401.06341/) (2024)
- [ContactGen: Generative Contact Modeling for Grasp Generation](https://mlsanity.com/paper/2310.03740/) (2023)
- [Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands](https://mlsanity.com/paper/2208.12250/) (2022)
- [Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking](https://mlsanity.com/paper/2406.04316/) (2024)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [One-Shot Open Affordance Learning with Foundation Models](https://mlsanity.com/paper/2311.17776/) (2023)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [Grasp as You Say: Language-guided Dexterous Grasp Generation](https://mlsanity.com/paper/2405.19291/) (2024)
- [Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation](https://mlsanity.com/paper/2306.08132/) (2023)
- [RealDex: Towards Human-like Grasping for Robotic Dexterous Hand](https://mlsanity.com/paper/2402.13853/) (2024)
- [UGG: Unified Generative Grasping](https://mlsanity.com/paper/2311.16917/) (2023)
- [Cross-Embodiment Dexterous Grasping with Reinforcement Learning](https://mlsanity.com/paper/2410.02479/) (2024)
- [AffordDexGrasp: Open-set Language-guided Dexterous Grasp with Generalizable-Instructive Affordance](https://mlsanity.com/paper/2503.07360/) (2025)
- [DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness](https://mlsanity.com/paper/2503.08257/) (2025)
- [Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding](https://mlsanity.com/paper/2408.13024/) (2024)

## Abstract (arXiv)

Achieving reliable robotic manipulation, such as dexterous grasping, requires a synergy between physically stable interactions and semantic task guidance, yet these objectives are often treated as separate, disjoint goals. In this paper, we investigate how to integrate dexterous grasping techniques, i.e., physically stable grasps for object lifting and language-guided grasp generation, to achieve both physical stability and semantic understanding. To this end, we propose SECOND-Grasp (SEmantic CONtact-guided Dexterous Grasping), a unified framework that enables robotic hands to dynamically adjust grasping strategies based on semantic reasoning while ensuring physical feasibility. We begin by obtaining coarse contact proposals through vision-language reasoning to infer where contacts should occur based on object properties, followed by segmentation to localize these regions across views. To further ensure consistency across multiple viewpoints, we introduce Semantic-Geometric Consistency Refinement (SGCR), which refines initial contact predictions by enforcing semantic consistency across views and removing geometrically invalid regions, yielding reliable 3D contact maps. Then, we derive a feasible hand pose for each contact map via inverse kinematics, generating a supervision signal for policy learning. Our approach, trained on DexGraspNet, consistently outperforms baselines in lifting success rate on both seen and unseen categories, achieving 98.2% and 97.7%, respectively, while also improving intent-aware grasping by 12.8% and 26.2%. We further show promising results on additional datasets and robotic hands, including Shadow Hand and Allegro Hand.