3D Polyhedron Pose Estimation for Secure Robot Grasping
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Solution Overview
Problem
Existing robot systems face inefficiencies and inaccuracies in detecting and grasping objects due to discrepancies between the robot's representation and the actual pose and size of objects, leading to unsuccessful or unsecure grasps, particularly in warehouse and logistics operations.
Innovation Solution
An end-to-end machine learning approach using multiple sensors and a 3D machine learning model to accurately predict the 3D translation, rotation, and size of polyhedrons, such as cuboids, by combining data from color and depth cameras to generate a joint feature map and correlation volume, enabling precise object interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object detection algorithms are used to detect 2D plane of objects, then the detection process is simpler, but the accuracy of pose and size representation deteriorates
Solution Approach 1:
The patent transitions from 2D object detection to 3D polyhedron detection by introducing depth information and spatial dimensions. The machine learning model predicts 3D bounding boxes with pose and size parameters, moving the detection from a single plane to three-dimensional space, thereby improving measurement precision while accepting increased algorithmic complexity.
Solution Approach 2:
The patent replaces traditional geometric and mechanical object representation methods with a machine learning-based approach. Instead of using simple 2D bounding boxes or manual pose estimation, the system employs trained neural networks to directly predict 3D polyhedron parameters, substituting computational mechanics with learned patterns from training data.
2Reliability
If robot uses inaccurate object representation, then the system operation is simpler, but the grasping success rate deteriorates
Solution Approach 1:
The patent combines multiple sensor inputs (depth camera, color camera) and integrates them into a unified machine learning processing pipeline. The depth information from the depth camera is merged with visual data, and both are processed together through the trained model to produce accurate 3D polyhedron representations, improving grasping reliability through multi-source data fusion.
Solution Approach 2:
The patent performs preliminary object detection and pose estimation using the trained machine learning model before the robot attempts grasping. By pre-processing the scene to identify accurate 3D polyhedron representations of target objects, the system prepares reliable grasp planning data in advance, thereby improving grasping success rate while managing system complexity through staged processing.
Data Source
AI summary
Methods and apparatus for controlling a mobile robot to perform an action are provided. The method includes receiving, by at least one computing device associated with a mobile robot, first sensor data and second sensor data, providing as input to at least one machine learning model, the first sensor data, the second sensor data, and camera intrinsics associated with at least one camera configured to sense the first sensor data and/or the second sensor data, wherein the at least one machine learning model is trained to output polyhedron information representing a set of objects in an environment of the mobile robot, and controlling the mobile robot to perform an action based, at least in part, on the polyhedron information output from the at least one machine learning model.