Robot Control Using Multi-Image Object Hierarchy Confidence
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Solution Overview
Problem
Existing robot control methods are error-prone and inflexible when dealing with densely cluttered environments, particularly in scenarios where objects are stacked, as they fail to account for uncertainties in object hierarchy inference from single images, leading to potential damage and inefficiency in object manipulation tasks.
Innovation Solution
A data-driven method using a neural network to acquire and combine multiple images, determining object hierarchy and confidence information, represented as adjacency matrices, to ensure sufficient confidence before manipulating objects, allowing for robust and adaptable control in densely cluttered environments by incrementally updating probabilistic representations of object relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object hierarchy information is determined from a single image, then the processing speed is fast, but the reliability of object manipulation is low due to insufficient confidence in stacking relations
Solution Approach 1:
The system performs preliminary actions by acquiring multiple images and determining object hierarchy information before the actual object manipulation occurs. This allows the robot to build a confident understanding of the stacking relations in advance, ensuring reliability before execution.
Solution Approach 2:
The system uses confidence information as feedback to determine whether additional images are needed. When the confidence in object hierarchy information is insufficient, the system automatically acquires additional images and updates the hierarchy information, creating a closed-loop feedback mechanism that ensures reliable manipulation decisions.
2Reliability
If additional images are acquired to improve confidence in object hierarchy, then the reliability increases, but the productivity decreases due to more image processing steps
Solution Approach 1:
The system applies partial action by acquiring only the necessary number of additional images based on the confidence threshold. It does not always acquire maximum images, but only enough to reach sufficient confidence levels, balancing reliability with productivity.
Solution Approach 2:
The system performs preliminary confidence assessment after each image acquisition and stops when the threshold is met, avoiding unnecessary additional image acquisitions that would reduce productivity without improving reliability further.
3Productivity
If the robot manipulates objects without sufficient confidence in stacking relations, then the operation speed is high, but harmful factors increase due to potential object damage
Solution Approach 1:
The system takes preliminary anti-action by acquiring multiple images and establishing confident object hierarchy information before manipulation, preventing potential object damage that would occur from incorrect assumptions about stacking relations.
Solution Approach 2:
The confidence information acts as feedback that prevents harmful actions. When confidence is insufficient, the system delays manipulation until additional images are acquired, thereby preventing potential object damage while maintaining overall productivity.
Data Source
AI summary
A method for controlling a robot device. The method includes acquiring an image(s) of in a workspace of the robot device; determining, by a neural network, object hierarchy information specifying stacking relations of the objects with respect to each other in the workspace of the robot device and confidence information for the object hierarchy information from the image(s); if the confidence information indicates a confidence above a confidence threshold, manipulating an object of the objects; if the confidence information indicates a confidence lower than the confidence threshold, acquiring an additional image of the objects and determining, by the neural network, additional object hierarchy information specifying stacking relations of the objects with respect to each other in the workspace of the robot device and additional confidence information for the additional object hierarchy information from the additional image and control the robot using the additional object hierarchy information.