Robot Visual Motion Prediction for Action-Conditioned Planning
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Solution Overview
Problem
Existing methods for robot interaction with the environment require manually labeled object information, making it impractical to scale real-world interaction learning to various scenes and objects, and existing video prediction techniques struggle with complex and stochastic physical interactions, particularly in real-world scenarios.
Innovation Solution
An action-conditioned motion prediction model that explicitly models pixel motion, predicting a distribution over pixel motion from previous frames, allowing robots to predict object motion without relying on object appearance, using datasets of robot interactions to learn physical interactions autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually labeled object information is used for robot interaction learning, then prediction accuracy can be improved, but the complexity and cost of data preparation increases significantly
Solution Approach 1:
The patent uses image copying and transformation techniques to generate training data. Instead of manually labeling objects, the system creates synthetic training examples by copying images and applying transformations to simulate different robot actions and outcomes, thereby eliminating the need for complex manual data preparation while maintaining prediction accuracy
Solution Approach 2:
The system performs self-service by automatically generating its own training data through image transformations and robotic execution of predicted actions. The robot autonomously creates training examples by executing predicted movements and capturing the resulting images, eliminating dependency on manually labeled datasets
2Ease of operation
If existing video prediction techniques are used for complex physical interactions, then implementation simplicity is maintained, but prediction accuracy deteriorates in real-world scenarios
Solution Approach 1:
The patent replaces traditional mechanical physics-based prediction models with a neural network-based visual prediction system. The neural network learns physical interaction patterns directly from image data, substituting complex mechanical modeling with data-driven learning that achieves higher accuracy while remaining implementationally simple
Solution Approach 2:
The system changes the parameters used for prediction from physics-based mechanical parameters to visual parameters captured in images. By training the neural network to recognize visual patterns of physical interactions, the system achieves accurate prediction of complex interactions without requiring explicit physics models
3Measurement precision
If short-term predictions are made for robot actions, then prediction accuracy is improved, but the ability to plan ahead is limited
Solution Approach 1:
The system performs preliminary predictions of multiple future states before the robot actually executes actions. By predicting short-term outcomes repeatedly in sequence, the robot can plan ahead multiple steps, effectively extending its planning horizon while maintaining the accuracy benefits of short-term predictions through iterative refinement
Data Source
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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.