Robot Dexterous Manipulation Policy via Virtual-Real Data Fusion
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Solution Overview
Problem
Existing robotic systems face challenges in achieving robust dexterous manipulation skills due to inefficiencies in real-time computation, inaccurate virtual models, sensor noise, and a significant sim-to-real gap in reinforcement learning methods.
Innovation Solution
A method that combines virtual simulations with real-world sensor data to derive policies for robotic manipulation. This involves performing virtual simulations to generate an initial policy, followed by real simulations to refine the policy based on sensor data, and then combining these policies to enhance the robot's dexterous manipulation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual simulations are used to learn dexterous manipulation skills, then computational efficiency is improved, but model accuracy deteriorates
Solution Approach 1:
The patent uses virtual simulations as simplified copies of real-world physics to enable efficient policy learning. The virtual model replicates essential dynamics without full physical complexity, allowing rapid computation while maintaining sufficient accuracy for manipulation tasks through careful model design and domain randomization
Solution Approach 2:
The patent performs preliminary policy learning in the virtual simulation environment before deploying to real hardware. This preliminary training phase allows the system to acquire basic manipulation skills computationally efficiently, then fine-tune using real sensor data to correct model inaccuracies
2Measurement precision
If reinforcement learning methods are used with real simulations, then model accuracy is improved, but training time deteriorates
Solution Approach 1:
The patent segments the training process into distinct phases: initial policy learning in virtual simulation, then iterative refinement using real sensor data. This segmentation allows the system to benefit from both fast virtual training and accurate real-world data without requiring exhaustive real simulation training
Solution Approach 2:
The patent uses partial real simulation data (only necessary correction steps) rather than exhaustive real-world training. The virtual simulation provides sufficient baseline performance, requiring only partial real data for fine-tuning, thus reducing overall training time while maintaining accuracy
3Speed
If virtual models are used for dexterous manipulation, then computational speed is improved, but robustness to sensor noise deteriorates
Solution Approach 1:
The patent incorporates feedback loops where real sensor data is used to correct and refine the virtual model's predictions. The system continuously compares virtual simulation outputs with actual sensor measurements and adjusts the policy accordingly, making the system robust to sensor noise while maintaining computational efficiency
Solution Approach 2:
The patent changes parameters of the virtual model through domain randomization and adaptive tuning based on real sensor data. By adjusting model parameters to match real-world conditions and accounting for sensor noise characteristics, the system achieves both computational speed and robustness
Data Source
AI summary
A method for dexterous manipulation by a robot includes performing a virtual simulation where a robot model adopts a virtual target position from a virtual initial position, deriving a first policy for maneuvering a robot based on the virtual simulation, performing a first set of real simulations where a first robot adopts a real target position from a real initial position based on the first policy, and deriving a second policy for maneuvering a robot based on sensor data generated in the first set of real simulations. The method also includes combining the first policy and the second policy to derive a third policy for maneuvering a robot. The method also includes causing at least one of the first robot and a second robot to adopt a real target position based on at least one of the third policy and a subsequently derived policy for maneuvering a robot.