Robot Motion Prediction From Images and Future Action Parameters
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Solution Overview
Problem
Robots face challenges in predicting how their actions affect objects in their environment without relying on manually labeled object information, which becomes impractical for scaling real-world interaction learning across various scenes and objects.
Innovation Solution
An action-conditioned motion prediction model that predicts pixel motion, allowing robots to 'visually imagine' different futures based on candidate movements by training on large datasets of robot interactions, using deep neural networks to transform images and predict future video sequences without reconstructing object appearance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots rely on manually labeled object information to predict object motion, then prediction accuracy improves, but scalability across various scenes and objects deteriorates
Solution Approach 1:
The patent uses image copying and transformation techniques where the current image is transformed based on predicted transformations to generate predicted images of future states. This allows the system to predict object motion without manually labeling objects, as the transformations are learned from raw pixel data across multiple frames, enabling scalability while maintaining prediction accuracy
Solution Approach 2:
The patent replaces traditional mechanical approaches of manual object labeling and detection with a data-driven neural network system. The system learns to predict object transformations directly from image sequences, substituting manual annotation processes with automated learning from raw visual data, thereby achieving both accuracy and scalability
2Adaptability or versatility
If deep neural networks are trained on large datasets of robot interactions, then generalization to unseen objects improves, but training data requirements and computational resources worsen
Solution Approach 1:
The patent performs preliminary actions by pre-training the neural network on large datasets of robot interactions before deployment. This preliminary training phase enables the network to learn generalizable patterns of object motion and transformation, so that when deployed, the system can handle unseen objects without requiring additional manual labeling or extensive fine-tuning
Solution Approach 2:
The patent creates a universal model that can handle multiple types of objects and scenes through a single trained neural network. The network learns general transformation patterns that apply across different objects, scenes, and robot actions, making the system multi-functional and highly generalizable without needing object-specific models
Data Source
AI summary
Some implementations of this specification are directed generally to deep machine learning methods and apparatus related to predicting motion(s) (if any) that will occur to object(s) in an environment of a robot in response to particular movement of the robot in the environment. Some implementations are directed to training a deep neural network model to predict at least one transformation (if any), of an image of a robot's environment, that will occur as a result of implementing at least a portion of a particular movement of the robot in the environment. The trained deep neural network model may predict the transformation based on input that includes the image and a group of robot movement parameters that define the portion of the particular movement.