Robot Movement Apparatus Using Weighted Regression Policy
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Solution Overview
Problem
Existing methods for robot reinforcement learning, such as Bayesian optimization and contextual covariance matrix adaptation evolutionary strategies, face challenges in achieving high sample efficiency and computing efficiency, which are crucial for simulating robot activity episodes that can be costly in terms of time and resources.
Innovation Solution
The proposed solution involves a training process that uses a cost function to achieve high sample efficiency and computing efficiency by weighting regression of samples to generate an upper policy, represented by a parameterized function. This process includes the use of a long short-term memory (LSTM) model for inference, which samples and generates the upper policy through weighted regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Bayesian optimization is used for contextual policy search, then sample efficiency is improved, but computing burden increases cubically to number of samples
Solution Approach 1:
The patent changes the mathematical parameters of the optimization approach by replacing Bayesian optimization with linear regression on transformed features. The contextual policy search is reformulated as a linear regression problem where the covariance matrix is factorized into a product of a lower triangular matrix and its transpose, enabling O(n) computing complexity instead of cubic complexity while maintaining sample efficiency
2Power
If contextual covariance matrix adaptation evolutionary strategies are used, then computing efficiency is improved, but sample efficiency decreases
Solution Approach 1:
The patent uses a simplified linear regression model that copies the essential functionality of more complex optimization methods but with reduced computational overhead. By representing the policy as a linear combination of basis functions and using efficient linear algebra operations, the method achieves both high sample efficiency and computing efficiency simultaneously
3Adaptability or versatility
If robot reinforcement learning is performed with existing methods, then robot movement adaptability is improved, but time cost increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing basis functions that can be efficiently combined to represent different policies. During execution, the system only needs to compute linear combinations of these pre-computed basis functions, significantly reducing the time required for policy evaluation and adaptation compared to re-computing everything from scratch
Data Source
AI summary
Apparatus, systems, articles of manufacture, and methods for robot movement are disclosed. An example robot movement apparatus includes a sequence generator to generate a sequence of context variable vectors and policy variable vectors. The context variable vectors are related to a movement target, and the policy variable vectors are related to a movement trajectory. The example apparatus includes a calculator to calculate an upper policy and a loss function based on the sequence. The upper policy is indicative of a robot movement, and the loss function is indicative of a degree to which a movement target is met. The example apparatus also includes a comparator to determine if the loss function satisfies a threshold and an actuator to cause the robot to perform the robot movement of the upper policy when the loss function satisfies the threshold.