Grasp Pose Detection

Table of contents

1. Core outputs
2. Typical workflow
3. Manipulation relevance

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Grasp pose detection predicts feasible gripper poses directly from sensory data.

Core outputs

• Candidate grasp poses
• Grasp quality/confidence scores
• Preferred gripper approach direction

Typical workflow

1. Segment target objects.
2. Generate grasp candidates in image or 3D space.
3. Rank by quality and collision constraints.
4. Execute best feasible grasp in a closed loop.

Manipulation relevance

• Reduces reliance on handcrafted grasp rules
• Works with cluttered and partially observed scenes
• Integrates well with perception-action pipelines