Polytopic Policy Optimization for Robust Learning-Based Feedback Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing reinforcement learning methods for robotic systems face challenges in robustness and stability due to model mismatch and poor sample efficiency, especially when interacting with novel objects, as they struggle to incorporate uncertainties in system dynamics during policy optimization.
Innovation Solution
A novel robust policy optimization mechanism that learns polytopic representations of uncertainty using Bayesian regression, allowing for the construction of robust policies through tractable semidefinite programs, and employs a throw-catch controller approach to drive systems to a terminal state with local stability guarantees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If model-free reinforcement learning methods are used to avoid complex dynamical models, then ease of operation is improved, but reliability deteriorates due to inability to guarantee robustness and stability
Solution Approach 1:
The system segments the control approach into two distinct components: a model-free reinforcement learning agent that handles exploration and adaptation, and a model-based safety layer that guarantees robustness and stability. This segmentation allows each component to specialize in its strength while the composite system achieves both ease of operation and reliability.
Solution Approach 2:
A safety layer acts as an intermediary between the model-free RL agent and the physical system. This intermediary layer verifies and corrects actions to ensure they satisfy robustness and stability requirements, thereby maintaining reliability while allowing the RL agent to operate with ease.
2Reliability
If model-based reinforcement learning is used to incorporate robustness and stability, then reliability is improved, but productivity deteriorates due to poor sample efficiency when model mismatch is large
Solution Approach 1:
The system divides the learning burden between the model-free RL agent that handles high-level decision-making and policy optimization, and the safety layer that handles verification and correction. This segmentation improves sample efficiency by avoiding the need for the model-based component to learn from scratch while maintaining robustness guarantees.
Solution Approach 2:
Instead of requiring a perfectly accurate dynamical model, the system uses a partial model that is sufficient for safety verification. The safety layer only needs to handle the uncertain portions of the dynamics, allowing the system to achieve robustness with less modeling effort and improved sample efficiency.
3Reliability
If region of attraction is designed to cover entire state-space, then reliability is improved, but device complexity increases making computation infeasible during learning
Solution Approach 1:
The safety layer focuses on ensuring stability within the relevant region of attraction that contains the operating trajectory, rather than computing the entire state-space. This local approach reduces computational complexity while maintaining reliability for the actual operating conditions.
Solution Approach 2:
The system pre-computes the region of attraction and safety constraints offline before the learning process begins. This preliminary action allows the safety layer to operate efficiently during learning by simply checking pre-computed conditions rather than performing complex computations in real-time.
4Reliability
If uncertainty representation is incorporated during controller design, then reliability is improved, but device complexity increases requiring suitable uncertainty representation for computation
Solution Approach 1:
The system transforms the uncertainty representation into parameters that are suitable for the safety layer verification. By changing the parameterization of uncertainty (e.g., using bounded disturbances or structured uncertainty models), the system achieves robustness guarantees while keeping the computational complexity manageable.
Data Source
AI summary
A controller is provided for generating a policy controlling a system by learning a dynamics of the system. The controller is configured to perform steps of acquiring measurement data from sensors arranged on the system, providing, to the memory, a non-linear system model represented by known part of the dynamics of the system and unknown part of the dynamics of the system, collecting states of the system by measuring the dynamics of the system using the sensors of the system based on a nominal policy and a noise term with respect to the states, estimating a sequence of sets of states of the system and sets of control inputs by collecting data of the system, wherein the data includes a collection of system states, applied control inputs and change in system states, wherein each of the control input is computed by the nominal policy and the additional noise term, learning a polytopic system by use of the collected data of the system for approximating the unknown part of the dynamics of the system using a linear probabilistic regression model, estimating an attractor basin of a terminal controller by sampling initial states in a neighborhood of a terminal state and estimating the attractor basin by supervised learning, and generating a polytopic policy using the estimated polytopic system to drive the system to the attractor basin of the terminal controller from an initial state.