Vision-Based Robot Control Using Synthetic Scenes and Continuous Motion
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Solution Overview
Problem
Conventional vision-based robot control systems face limitations in adaptability, precision, and fluid movement in dynamic or unstructured environments due to reliance on fixed success criteria, predefined object models, discrete action spaces, and imprecise training datasets.
Innovation Solution
A vision-based robot control model is trained using simulated data from diverse environments, generating robot plans with continuous action spaces, enabling high precision and adaptability without requiring predefined object models, and using context encoders and decoders to process multi-modal sensor inputs for precise robot movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined object models and CAD files are used for object localization, then object detection accuracy is improved, but adaptability to novel or partially visible objects deteriorates
Solution Approach 1:
The patent uses synthetic training data that copies real-world object appearances and scenarios into virtual environments. This allows the model to learn object recognition and manipulation from simulated observations, enabling it to handle novel objects without requiring predefined CAD models or real-world training data for each specific object type.
Solution Approach 2:
The patent replaces traditional mechanical vision pipelines (which rely on hand-crafted features and predefined models) with a neural network-based system that processes synthetic images directly. This substitution allows the system to infer object properties and relationships from synthetic data, achieving both accuracy and adaptability simultaneously.
2Ease of operation
If discrete action spaces are used for robot control, then control simplicity is improved, but movement fluidity and precision deteriorate
Solution Approach 1:
The patent employs dynamic action spaces where the robot controller can select from continuous ranges of motion rather than fixed discrete actions. This dynamic approach allows the system to generate fluid, precise movements adapted to the current synthetic scene, while the training process simplifies the control logic through reinforcement learning from simulated feedback.
Solution Approach 2:
The patent changes the action space parameters from discrete to continuous, enabling the robot to perform smooth, variable-speed movements. The training system uses parameterized action representations that can be optimized through reinforcement learning, achieving both simplicity in the learned policy and precision in the executed movements.
3Ease of manufacture
If fixed success criteria are used for task completion, then evaluation simplicity is improved, but task adaptability in dynamic environments deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the robot receives simulated sensory feedback about its actions and the resulting changes in the environment. This feedback loop allows the reinforcement learning agent to learn adaptive task completion strategies that work across diverse synthetic scenes, while the evaluation framework maintains simplicity through standardized success criteria that are automatically checked in the simulation environment.
4Ease of manufacture
If imprecise training datasets are used, then data collection ease is improved, but robot movement precision deteriorates
Solution Approach 1:
The patent creates synthetic training data by copying real-world physics, lighting conditions, and object relationships into virtual environments. This synthetic data generation process is easier than collecting real-world data for every possible scenario, while the precision is maintained through high-fidelity rendering and accurate simulation of physical interactions, providing both ease of data collection and high movement precision.
Data Source
AI summary
Techniques for training a vision-based robot control model include generating, based on scene data, a plurality of scenes, generating, based on the plurality of scenes, one or more goal specifications, determining, based on the one or more goal specifications and a robot model, one or more robot plans, generating, based on the one or more robot plans and the plurality of scenes, simulated sensor data, and performing one or more training operations to generate a trained vision-based robot control model based on the one or more goal specifications, the one or more robot plans, and the simulated sensor data.