Robot Grasp Planning with Platonic Shape Abstraction
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Solution Overview
Problem
Current robot systems face challenges in effectively grasping and engaging with objects in diverse environments due to limitations in sensing and control mechanisms, particularly in identifying suitable grasp locations and applying appropriate grasp primitives based on object shapes.
Innovation Solution
A robot system equipped with sensors and a controller that captures sensor data, generates a platonic representation of objects using three-dimensional shapes, selects an appropriate grasp primitive, and controls the end effector to grasp the object at a selected location, optimizing actuation based on real-time sensor feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robot systems use conventional sensing and control mechanisms to grasp objects, then the system structure remains simple, but the ability to identify suitable grasp locations and apply appropriate grasp primitives is insufficient
Solution Approach 1:
The system segments the object recognition and grasp planning process into distinct modules: sensor data acquisition, platonic representation generation, grasp primitive selection, and end effector control. This modular segmentation enables complex grasping capabilities while maintaining manageable system architecture through functional decomposition
Solution Approach 2:
The patent introduces an intermediary platonic representation layer between sensor data and grasp primitive selection. This intermediate geometric abstraction serves as a mediator that translates complex sensor data into simplified shape representations, enabling more effective grasp planning without directly increasing physical system complexity
2Manufacturing precision
If robot systems use basic grasp control without simulation, then the control process is fast, but the accuracy of grasping is insufficient
Solution Approach 1:
The system performs preliminary grasp simulation and evaluation before executing the actual grasp action. By pre-evaluating grasp effectiveness through simulation, the system ensures high grasping accuracy while the actual execution remains efficient, as the computationally intensive simulation occurs beforehand
Solution Approach 2:
The system uses its own simulation capability to evaluate and select optimal grasp primitives autonomously. The robot controller performs self-evaluation of grasp effectiveness using simulated physics and geometry, enabling accurate grasp selection without external intervention or iterative trial-and-error
Data Source
AI summary
Systems, methods, and control modules for controlling robot systems are described. An object is represented by a platonic representation, which is one or more basic geometric shapes which approximate the object. A library of ways to grasp these basic geometric shapes is accessed, and an appropriate way to grasp a shape is selected and used to grasp the object at a location where the basic geometric shape at least approximately corresponds to the grasp.