Object Grasping Keypoint Subdivision for Overlapping Targets
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Solution Overview
Problem
Current robotics systems face challenges in accurately grasping objects, especially when they are overlapping or in random poses, due to difficulties in determining optimal grasp locations and handling occluded or complex geometries.
Innovation Solution
The method generates keypoints for detected objects, subdivides them into subregions, determines graspability scores, and selects candidate grasp locations based on these scores to improve grasping accuracy and speed, even in complex scenarios like overlapping objects or internal cavities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system attempts to grasp objects in complex scenarios (overlapping, random poses, occluded), then the grasping capability is improved, but the accuracy of determining optimal grasp locations deteriorates
Solution Approach 1:
The system segments the object surface into multiple subregions and evaluates graspability for each subregion independently. This allows the system to handle complex scenarios by breaking down the overall grasping task into manageable local evaluations, identifying visible faces and avoiding difficult-to-grasp features while maintaining overall accuracy.
2Reliability
If the system evaluates all possible grasp locations to ensure accuracy, then the grasping success rate is improved, but the runtime efficiency deteriorates
Solution Approach 1:
The system applies different evaluation criteria and graspability metrics to different subregions of the object. By determining graspability scores for each subregion based on local geometric features and visibility, the system efficiently identifies optimal grasp locations without exhaustively evaluating all possible locations, thus maintaining high success rates while improving runtime efficiency.
3Adaptability or versatility
If the system uses detailed geometric analysis to handle complex geometries, then the handling of occluded objects is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary evaluation of subregion visibility and graspability before executing the full grasping algorithm. By pre-identifying visible faces and filtering out difficult-to-grasp features in advance, the system reduces the computational burden of handling complex geometries and occluded objects, maintaining adaptability while reducing computational complexity.
Data Source
AI summary
A method for object grasping can include: generating a set of keypoints for one or more detected objects in a scene; subdividing the set of keypoints into subsets, each corresponding to a subregion of a detected object; determining a graspability score for the subregion; determining a grasp location for the subregion; selecting a candidate grasp location; and optionally grasping an object using the candidate grasp location.