3D Shape Completion Model for Occluded Object Manipulation
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Solution Overview
Problem
Manipulating objects with incomplete or noisy sensor data, especially when partially occluded, leads to inaccurate models and poor success rates in robotics.
Innovation Solution
A system and method for shape completion using a processor to transform sensor data into a voxel grid, encode it into a partial latent vector, determine a mapping to a complete latent space, and predict a complete object shape, incorporating a multi-modal 3D shape completion model that accepts various sensors like RGB-D and tactile sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensor data is collected when object is grasped, then object manipulation is enabled, but sensor data becomes incomplete due to occlusion by grasp devices
Solution Approach 1:
The system performs shape completion prediction before actual manipulation tasks. By predicting the complete shape of the occluded object in advance using autoencoders and generative models, the system prepares accurate geometric models that will be used for subsequent grasping and manipulation, thus avoiding the information loss problem during actual operation.
Solution Approach 2:
The system creates a complete virtual copy of the occluded object through shape completion algorithms. Instead of relying on incomplete sensor data, the autoencoder-based model generates a full 3D representation that replicates the complete object geometry, enabling accurate manipulation planning without direct observation of all object surfaces.
2Adaptability or versatility
If object is partially occluded during sensing, then manipulation scenarios become more realistic, but model accuracy deteriorates
Solution Approach 1:
The system introduces latent space representations as intermediaries between incomplete sensor data and final object models. The autoencoder compresses partial point cloud data into latent vectors, and generative models in the latent space reconstruct complete object shapes, thereby mediating the information gap caused by occlusion and restoring model accuracy.
Solution Approach 2:
The system transforms the representation parameters of object data from incomplete 3D point clouds to complete latent space vectors and back to full 3D models. By changing the parameter space and using learned transformations, the system recovers accurate geometric parameters even when input sensor data is incomplete due to occlusion.
3Device complexity
If traditional shape reconstruction methods are used, then processing is simpler, but success rate of manipulation tasks decreases
Solution Approach 1:
The system replaces traditional geometric reconstruction algorithms with machine learning-based autoencoders and generative models. Instead of using deterministic geometric methods that fail with incomplete data, the system employs probabilistic deep learning models that can infer complete shapes from partial observations, significantly improving manipulation success rates despite increased computational complexity.
Data Source
AI summary
Systems and methods for shape completion are provided. In one embodiment, a computer implemented method includes receiving sensor data for a visualized area of an object as at least one point cloud representation. The computer implemented method also includes transforming the at least one point cloud representation into an input voxel grid of the visualized area of the object. The input voxel grid is a volumetric representation. The computer implemented method further includes encoding the input voxel grid into a partial latent vector that lies on a partial latent space. The computer implemented method yet further includes determining a mapping between the partial latent space and a complete latent space based on the sensor data. The computer implemented method includes predicting a complete latent vector based on the complete latent space. The computer implemented method also includes estimating a complete shape of an object based on the complete latent space.