# Grasping Calibration

> Generated from `library/paper_cards/`. Do not edit this page directly; edit paper cards and rerun `vp run docs:generate`.

Grasp synthesis, grasp detection and planning, closed-loop grasping, and hand-eye calibration.

## Paper Cards

| ID | Year | Status | Title |
| --- | ---: | --- | --- |
| [GRASP-001](/paper-library/cards/grasp-001) | 2014 | `extracted` | Data-Driven Grasp Synthesis - A Survey |
| [GRASP-003](/paper-library/cards/grasp-003) | 2015 | `extracted` | Real-Time Grasp Detection Using Convolutional Neural Networks |
| [GRASP-004](/paper-library/cards/grasp-004) | 2017 | `extracted` | Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics |
| [GRASP-005](/paper-library/cards/grasp-005) | 2018 | `extracted` | Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach |
| [GRASP-006](/paper-library/cards/grasp-006) | 1989 | `extracted from OCR` | A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration |
| [GRASP-007](/paper-library/cards/grasp-007) | 2018 | `extracted` | Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection |

## Use In SAH-BRI-Grasp

Use this page to locate source cards for evidence review. Promote claims through `library/EVIDENCE_MATRIX.md` before using them as manuscript-level claims.