Robot Control Policy Training With SDF Rewards and Adaptive Curriculum
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Solution Overview
Problem
Conventional robot training methods using simulation data can lead to errors such as interpenetrations and over/under-specific rewards, resulting in improperly trained models that fail to correctly control physical robots in real-world environments, and curriculum learning may struggle to transition from easy to difficult tasks.
Innovation Solution
A sampling-based curriculum and signed distance field (SDF)-based reward approach is used to train machine learning models, accounting for simulation errors and providing a balanced reward, allowing the model to learn the entire range of task difficulties and adapt to real-world scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If training data is generated via robot simulations to avoid damage to physical robots and objects, then safety and cost are improved, but simulation errors such as interpenetrations occur leading to improper training
Solution Approach 1:
The patent converts the harmful simulation errors (interpenetrations) into beneficial training signals by computing rewards based on error magnitudes. The error, which would normally indicate a flawed simulation, is instead used to provide corrective feedback to the machine learning model, turning a disadvantage into a learning opportunity that improves training reliability while maintaining simulation-based safety.
2Stability of the object's composition
If curriculum learning is used to train models on easy tasks first, then training stability is improved, but models struggle to transition to difficult tasks
Solution Approach 1:
The patent applies dynamics by making the curriculum adaptive rather than static. The system dynamically adjusts task difficulty based on model performance, allowing smooth transitions between easy and difficult tasks. This dynamic approach maintains training stability while improving adaptability to the full range of task difficulties.
Solution Approach 2:
The patent changes parameters by using error-based reward computation that adapts to different task difficulties. The reward magnitude and computation method are adjusted based on the observed simulation errors, enabling the model to effectively learn across varying task complexities without struggling at transition points.
3Device complexity
If conventional reward computation is used in simulations, then training process is simple, but rewards are over/under-specific leading to poor real-world performance
Solution Approach 1:
The patent introduces feedback by computing rewards based on actual simulation errors rather than conventional predetermined reward functions. The error magnitude provides feedback about the quality of actions, creating a more accurate and reliable reward signal that reflects real-world physics, thereby improving real-world task performance while maintaining reasonable computational complexity.
Data Source
AI summary
One embodiment of a method for training a machine learning model to control a robot includes causing a model of the robot to move within a simulation based on one or more outputs of the machine learning model, computing an error within the simulation, computing at least one of a reward or an observation based on the error, and updating one or more parameters of the machine learning model based on the at least one of a reward or an observation.