Inverse Optimal Control with Constraint Relaxation

TL;DR

This paper introduces a novel inverse optimal control method using constraint relaxation and penalty functions to better handle noisy demonstrations and inequality constraints, improving estimation accuracy in constrained environments.

Contribution

It proposes a new IOC approach with constraint relaxation via penalty functions, effectively managing noisy data and incorrect constraint activations.

Findings

Outperforms traditional methods in simulation with noisy demonstrations

Reduces the number of unknown variables in estimation

Enhances robustness to constraint activation errors

Abstract

Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their intentions, by estimating an unknown objective function from one or more corresponding optimal control sequences. When computing estimates from demonstrations in environments with safety-preserving inequality constraints, acknowledging their presence in the chosen IOC method is crucial given their strong influence on the final control strategy. However, solution strategies capable of considering inequality constraints, such as the inverse Karush-Kuhn-Tucker approach, rely on their correct activation and fulfillment; a restrictive assumption when dealing with noisy demonstrations. To overcome this problem, we leverage the concept of exact penalty functions for IOC and show preservation of estimation accuracy. Considering noisy demonstrations, we then illustrate how the usage of penalty functions reduces the number of unknown variables and how their approximations enhance the estimation method's capacity to account for wrong constraint activations within a polytopic-constrained environment. The proposed method is evaluated for three systems in simulation, outperforming traditional relaxation approaches for noisy demonstrations.