Robotic Grasp Selection Using Pairwise Ranking Heuristics
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Solution Overview
Problem
Current robotic grasp selection methods are inefficient in automatically determining the optimal grasp of an object-of-interest, particularly in environments with complex geometries, as they fail to accurately consider user preferences and object-environment interactions.
Innovation Solution
A pairwise ranking method that calculates heuristics to describe the relationship between candidate grasps and the object-of-interest and environment, using user preferences to rank grasps through a series of pairwise comparisons, ensuring that the selected grasp is safe, efficient, and aligned with user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current robotic grasp selection methods are used, then the system can identify candidate grasps, but it fails to accurately determine the optimal grasp when considering user preferences and object-environment interactions
Solution Approach 1:
The grasp selection process is segmented into multiple independent components: candidate grasp generation, heuristic calculation (with sub-components for object relationship and environment relationship), and pairwise ranking. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
Heuristics serve as intermediary variables that mediate between the complex input data (candidate grasps, object properties, environment constraints) and the final selection decision. These heuristics translate complex relationships into comparable metrics that enable accurate pairwise ranking without requiring the system to directly process all input variables simultaneously.
2Reliability
If pairwise ranking with multiple heuristics is implemented, then grasp success rate improves, but computational time increases
Solution Approach 1:
The system calculates only the necessary heuristics required for pairwise comparison rather than computing all possible grasp attributes. For each pairwise comparison, only the relevant heuristics affecting that specific comparison are computed, reducing unnecessary computational overhead while maintaining selection accuracy.
Solution Approach 2:
Heuristics are pre-calculated for all candidate grasps before the pairwise ranking process begins. This preliminary calculation organizes the data in advance, allowing the ranking algorithm to operate efficiently on pre-processed information rather than computing metrics on-the-fly during comparisons.
3Manufacturing precision
If the system considers environment interactions and user preferences, then grasp quality improves, but the complexity of determining relationships increases
Solution Approach 1:
The heuristic calculation focuses on local relationships between the grasp and specific aspects of the object and environment, rather than attempting to model all global interactions. Each heuristic evaluates a specific local aspect (e.g., orientation relative to object surface, distance to environment obstacles), making the complex problem tractable through localized analysis.
Solution Approach 2:
The system transforms complex relationship determinations into simplified parameter comparisons. By converting environmental and preference constraints into quantitative heuristic parameters, the system can efficiently compare candidate grasps using numerical values rather than complex qualitative assessments.
Data Source
AI summary
A method for selecting a preferred robotic grasp of an object-of-interest using pairwise ranking includes: identifying a robotic candidate grasp usable to grasp the object-of-interest, the object-of-interest situated in an environment; receiving a grasp preference for a preferred candidate grasp of the object-of-interest; calculating a heuristic to describe a relationship of the candidate grasp to one or more of the object-of-interest and the environment; and using the heuristic and using the grasp preference, computing a pairwise ranking of two candidate grasps to determine an ordering of the at least two candidate grasps.