Robotic Rearrangement via Neural Network Pose Prediction
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Solution Overview
Problem
Existing robotic systems struggle to rearrange objects in real-world scenarios without access to models of the objects, and often require additional context or target images, which may not be feasible.
Innovation Solution
A method and system that utilize neural networks to process point clouds and natural language instructions, predicting pose offsets to rearrange objects based on the instructions, without the need for explicit object models or target images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If robotic systems use prior work methods requiring target images or object models, then rearrangement accuracy is improved, but system complexity and user burden increase
Solution Approach 1:
The patent uses neural networks to learn from example rearrangements and generate pose offsets without requiring explicit target images or object models. The system copies the semantic understanding and spatial reasoning capabilities from training data to perform novel rearrangement tasks autonomously.
Solution Approach 2:
The patent replaces traditional mechanical vision-based approaches (requiring target images and object models) with a neural network-based semantic understanding system that processes natural language instructions and point cloud data to directly predict rearrangement poses.
2Manufacturing precision
If robotic systems require additional context or target images for rearrangement tasks, then task accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts and removes the requirement for target images and object models from the system input. Instead, it uses only natural language instructions and point cloud data as input, significantly simplifying the operation interface while maintaining rearrangement accuracy through neural network-based semantic reasoning.
Solution Approach 2:
The neural network system provides universal rearrangement capability across different objects and scenes without requiring object-specific models or task-specific target images. The same system architecture handles various rearrangement tasks by learning from diverse training data.
3Ease of operation
If robotic systems operate without object models or target images, then ease of operation is improved, but rearrangement precision deteriorates
Solution Approach 1:
The patent incorporates a discriminator network that provides feedback on the generated pose offsets, evaluating whether the predicted rearrangements satisfy the natural language instruction. This feedback mechanism enables the system to iteratively refine its predictions and achieve high precision without requiring explicit object models or target images.
Solution Approach 2:
The system changes the input parameters from traditional computer vision data (target images, object models) to semantic data (natural language instructions, point cloud representations). This parameter transformation enables the neural network to perform precise rearrangement prediction based on semantic understanding rather than pixel-level matching.
Data Source
AI summary
A robotic system is provided for performing rearrangement tasks guided by a natural language instruction. The system can include a number of neural networks used to determine a selected rearrangement of the objects in accordance with the natural language instruction. A target object predictor network processes a point cloud of the scene and the natural language instruction to identify a set of query objects that are to-be-rearranged. A language conditioned prior network processes the point cloud, natural language instruction, and the set of query objects to sample a distribution of rearrangements to generate a number of sets of pose offsets for the set of query objects. A discriminator network then processes the samples to generate scores for the samples. The samples may be refined until a score for at least one of the sample generated by the discriminator network is above a threshold value.