Robot Control Policy Optimization via Balanced Spinner Gradient Estimation
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Solution Overview
Problem
Existing methods for determining control policy parameters for robots are inefficient and prone to noise, especially in complex continuous control tasks, leading to slow convergence and high computational expense.
Innovation Solution
The use of structured orthogonal matrices, specifically balanced spinners, to define perturbation directions for estimating gradients and Jacobians in finite difference procedures, allowing for more robust and efficient optimization of control policies in simulation environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard finite difference procedures are used to estimate gradients and Jacobians, then the optimization process can proceed, but the convergence is slow and computational expense is high
Solution Approach 1:
The patent changes the parameters used in finite difference estimation from standard coordinate directions to structured orthogonal directions defined by balanced spinners. This parameter change in the perturbation directions significantly improves gradient estimation quality, leading to faster convergence and reduced computational expense while maintaining the same optimization framework
Solution Approach 2:
The patent substitutes the standard finite difference mechanism with a structured finite difference approach using balanced spinners. This substitution replaces the inefficient coordinate-direction-based perturbation with an optimized structured perturbation method that achieves better convergence properties without fundamental changes to the optimization algorithm structure
2Reliability
If finite difference procedures are used to estimate gradients in noisy simulation environments, then optimization can proceed, but the estimates are noisy and convergence is unreliable
Solution Approach 1:
The patent changes the perturbation directions from standard coordinate directions to structured orthogonal directions defined by balanced spinners. This parameter change provides more stable and less noisy gradient estimates in simulation environments, improving both the precision of gradient estimation and the reliability of optimization convergence
Solution Approach 2:
The balanced spinner matrices serve as an intermediary structure that mediates between the noisy simulation environment and the gradient estimation process. By introducing this structured intermediate representation for perturbation directions, the method filters out some noise and provides more reliable gradient estimates
3Manufacturing precision
If complex constraints are incorporated into optimization, then the solution is more accurate for real-world tasks, but the optimization becomes more difficult and computationally expensive
Solution Approach 1:
The patent substitutes standard finite difference methods with structured finite difference using balanced spinners, which improves the efficiency of handling complex constraints. The structured perturbation directions enable better exploration of the constraint boundary and more efficient convergence to accurate solutions without requiring fundamental changes to constraint handling mechanisms
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the determination of control policies for robots through the performance of simulations of robots and real-world context to determine control policy parameters.