Robot-Carried Object Referencing for Accurate Post-Grasp Pose
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional robotics systems struggle with accurately determining the pose of objects after they have been grasped, leading to inaccuracies and potential collisions due to unpredictable shifting during and after grasping, which complicates tasks requiring precise object positioning and can compromise safety.
Innovation Solution
The system employs a method that includes using information about an expected object pose, repositioning the object for perception data gathering, and processing estimates from a neural network to generate accurate object references, utilizing three-dimensional information to register a known model with real-time pose data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional robotics systems use standard object tracking methods, then the system complexity remains low, but the measurement precision of object pose deteriorates due to unpredictable shifting during and after grasping
Solution Approach 1:
The system performs preliminary actions by gathering perception data at multiple poses before the object is grasped, and generates object references in advance. This preliminary data collection and processing enables accurate pose estimation even when the object shifts during grasping, resolving the contradiction between maintaining low system complexity and achieving high measurement precision.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing expected object poses with actual perception data gathered at multiple poses. This feedback loop allows the system to detect and compensate for object shifting during grasping, improving measurement precision without significantly increasing system complexity through iterative refinement of pose estimates.
2Measurement precision
If the system gathers perception data at multiple poses before grasping, then the object pose estimation accuracy improves, but the loss of time increases due to additional data collection steps
Solution Approach 1:
The system performs preliminary perception data gathering at multiple poses before the grasping action occurs. By collecting and processing this data in advance, the system establishes accurate object references that remain valid even if the object shifts during grasping. This preliminary action approach improves measurement precision while minimizing time loss during the actual grasping and manipulation tasks.
3Productivity
If the system uses expected object pose information to generate references, then the productivity increases by reducing collisions and improving task precision, but the reliability may deteriorate if the expected pose does not match the actual pose due to object shifting
Solution Approach 1:
The system generates object references based on perception data gathered at multiple poses before grasping, rather than relying solely on expected poses. This preliminary data collection creates more reliable references that account for actual object positions, reducing collisions and improving task precision while maintaining reliability even when object shifting occurs during manipulation.
Data Source
AI summary
A method in accordance with a particular embodiment of the present technology includes retrieving an object from a starting location by grasping the object between two end effectors of a mobile robot. The method further includes generating, by data-processing hardware operably associated with the mobile robot, an object reference corresponding to a pose of the object at a time after retrieving the object and while the object is in contact with the end effectors. Generating the object reference is based at least partially on perception data corresponding to the object and includes generating a three-dimensional reference corresponding to a feature of the object based at least partially on a two-dimensional estimate from a neural network and a three-dimensional reference corresponding to a different feature of the object. Finally, the method includes controlling the mobile robot based at least partially on the object reference.