Safe Pontryagin Differentiable Programming
Wanxin Jin, Shaoshuai Mou, George J. Pappas

TL;DR
Safe Pontryagin Differentiable Programming (Safe PDP) provides a theoretical framework for safety-critical learning and control tasks, ensuring safety constraints are satisfied at all stages through barrier functions and efficient approximations.
Contribution
It introduces a novel Safe PDP methodology that guarantees safety throughout learning and control by integrating barrier functions and efficient unconstrained problem solutions.
Findings
Successfully applied to safe policy optimization
Effective in safe motion planning for complex systems
Demonstrated safety guarantees in learning MPCs from demonstrations
Abstract
We propose a Safe Pontryagin Differentiable Programming (Safe PDP) methodology, which establishes a theoretical and algorithmic framework to solve a broad class of safety-critical learning and control tasks -- problems that require the guarantee of safety constraint satisfaction at any stage of the learning and control progress. In the spirit of interior-point methods, Safe PDP handles different types of system constraints on states and inputs by incorporating them into the cost or loss through barrier functions. We prove three fundamentals of the proposed Safe PDP: first, both the solution and its gradient in the backward pass can be approximated by solving their more efficient unconstrained counterparts; second, the approximation for both the solution and its gradient can be controlled for arbitrary accuracy by a barrier parameter; and third, importantly, all intermediate results…
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Code & Models
Videos
Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Control Systems Optimization · Reinforcement Learning in Robotics
MethodsRandom Convolutional Kernel Transform
