Robot Grasp Control Using Platonic Shape Representations
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Solution Overview
Problem
Current robot systems face challenges in effectively grasping and manipulating objects in diverse environments due to limitations in sensing and control methods, which hinder precise object recognition and optimal grasp selection.
Innovation Solution
A robot system equipped with sensors and a controller that captures sensor data to generate a platonic representation of objects using three-dimensional shapes, selects appropriate grasp primitives based on these representations, and controls end effectors to grasp objects at optimal locations, enhancing grasp effectiveness through simulation and feedback adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robot systems use conventional sensing and control methods to grasp objects, then the system structure remains simple, but the grasping precision and object recognition accuracy deteriorate
Solution Approach 1:
The patent creates a platonic representation (a simplified geometric model) of the target object based on sensor data. This copy captures the essential grasping-relevant features (shape, size, orientation) without requiring complete geometric fidelity. The robot controller then selects grasp primitives from a library that are matched to this platonic representation, enabling accurate grasp planning without needing to model every detail of the actual object.
Solution Approach 2:
The patent segments the grasping task into distinct components: (1) sensing raw object data, (2) generating a platonic representation from sensor data, (3) selecting appropriate grasp primitives from a library based on the representation, and (4) executing the grasp. This segmentation allows each component to be optimized independently, improving overall grasping precision without proportionally increasing overall system complexity.
2Reliability
If robot systems use conventional grasp selection methods, then the control process remains simple, but the grasp effectiveness and reliability deteriorate
Solution Approach 1:
The patent pre-computes and stores multiple grasp primitives in a library, each associated with specific platonic representations of objects. Before actual grasping occurs, the system has already prepared appropriate grasp strategies for various object types. During execution, the robot controller only needs to match the current object's platonic representation to the pre-computed library entries, significantly improving grasp reliability without requiring complex real-time optimization.
Solution Approach 2:
The patent transforms raw sensor data into a standardized platonic representation format with specific parameters (shape category, dimensions, orientation). This parameter transformation enables the robot controller to efficiently query the grasp primitive library using standardized keys, improving both reliability and speed of grasp selection while maintaining manageable control complexity through consistent data formatting.
3Measurement precision
If robot systems handle diverse objects with generic methods, then the system versatility is maintained, but the grasping precision for specific object types deteriorates
Solution Approach 1:
The patent creates a universal platonic representation framework that can represent diverse object types (spheres, cubes, cylinders, irregular shapes) using a common set of geometric parameters and shape categories. The grasp primitive library is designed to cover multiple object types, with each primitive potentially applicable to various platonic representations. This universality allows the system to maintain high grasping precision across diverse objects without requiring separate specialized algorithms for each object type.
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.