Robot Manipulation Control for Low-Confidence Stacked Objects
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Solution Overview
Problem
Current robot control methods are inadequate for manipulating objects in densely cluttered environments, as they fail to accurately determine object hierarchy and confidence levels, leading to uncertain and error-prone operations when dealing with stacked objects.
Innovation Solution
A method using a neural network to acquire and combine image data, determining object hierarchy information and confidence levels, and adjusting robot control based on probabilistic representations of stacking relations, ensuring sufficient confidence thresholds are met before manipulation, and iteratively refining this information through additional image acquisition if initial confidence is low.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robot control methods are used in densely cluttered environments, then object manipulation capability is improved, but accuracy in determining object hierarchy deteriorates
Solution Approach 1:
The system performs preliminary actions by acquiring multiple images from different viewpoints before making manipulation decisions. The neural network processes these images to determine object hierarchy information and confidence levels in advance, allowing the robot to plan manipulation sequences accurately even in cluttered environments where single-image analysis would fail.
Solution Approach 2:
The system transitions from two-dimensional single-image analysis to three-dimensional multi-viewpoint analysis. By capturing images from multiple angles and processing them through a neural network, the system reconstructs spatial relationships and object hierarchies in 3D space, significantly improving hierarchy determination accuracy in densely cluttered environments.
2Reliability
If additional image acquisition is performed to improve confidence, then object hierarchy information accuracy is improved, but operation time increases
Solution Approach 1:
The system implements a feedback mechanism where the neural network outputs confidence information for determined object hierarchy relationships. Based on whether this confidence exceeds a threshold, the system decides whether to acquire additional images or proceed with manipulation, creating a closed-loop control that balances accuracy and efficiency dynamically.
Solution Approach 2:
The image acquisition process is made dynamic and adaptive rather than static and fixed. The system adjusts the number of images to acquire based on real-time confidence assessments from the neural network, acquiring additional images only when necessary to achieve sufficient confidence, thereby minimizing operation time while maintaining reliability.
3Object-affected harmful factors
If confidence threshold is set high to ensure safety, then manipulation safety is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary confidence assessments and additional image acquisitions before manipulation to ensure high confidence levels are achieved in advance. This preliminary verification ensures manipulation safety by confirming object hierarchy relationships with high confidence before the robot executes potentially harmful actions.
Solution Approach 2:
The system maintains continuous operation by parallelizing image acquisition and neural network processing while the robot prepares for manipulation. Once confidence thresholds are met, the system proceeds immediately with manipulation without idle waiting time, ensuring that the high safety standards do not significantly impact overall productivity.
Data Source
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AI summary
According to various embodiments, a method for controlling a robot device is described comprising acquiring at least one image of a plurality of objects in a workspace of the robot device; determining, by a neural network, object hierarchy information specifying stacking relations of the plurality of objects with respect to each other in the workspace of the robot device and confidence information for the object hierarchy information from the at least one image, if the confidence information indicates a confidence for the object hierarchy information above a confidence threshold, manipulating an object of the plurality of objects, if the confidence information indicates a confidence for the object hierarchy information lower than the confidence threshold acquiring an additional image of the plurality of objects and determining, by the neural network, additional object hierarchy information specifying stacking relations of the plurality of 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.