@workingpaper{wang2025risk,

title = {Risk-Sensitive Model Predictive Control for Interaction-Aware Planning--A Sequential Convexification Algorithm},

author = {Renzi Wang and Mathijs Schuurmans and Panagiotis Patrinos},

url = {https://doi.org/10.48550/arXiv.2503.14328},

year  = {2025},

date = {2025-03-18},

abstract = {This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigating a corridor demonstrate the effectiveness and the computational advantage of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {workingpaper}

}

@workingpaper{wang2024imitation,

title = {Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach},

author = {Renzi Wang and Flavia Sofia Acerbo and Tong Duy Son and Panagiotis Patrinos},

url = {https://doi.org/10.48550/arXiv.2411.08232

},

year  = {2024},

date = {2024-11-12},

abstract = {This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The parameters of the model are learned via a two-stage framework. By leveraging the existing dynamics knowledge, the first stage of the framework estimates the control input sequences and hence reduces the problem complexity. At the second stage, the policy is learned by solving a regularized maximum-likelihood estimation problem using the estimated control input sequences. We further extend the learning procedure by incorporating a Lyapunov stability constraint to ensure asymptotic stability of the identified model, for accurate multi-step predictions. The effectiveness of the proposed framework is validated using two autonomous driving datasets collected from human demonstrations, demonstrating its practical applicability in modelling complex nonlinear dynamics.},

keywords = {},

pubstate = {published},

tppubtype = {workingpaper}

}

@workingpaper{wang2024em++,,

title = {EM++: A parameter learning framework for stochastic switching systems},

author = {Renzi Wang and Alexander Bodard and Mathijs Schuurmans and Panagiotis Patrinos},

url = {https://doi.org/10.48550/arXiv.2407.16359

},

year  = {2024},

date = {2024-07-01},

abstract = {This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of distributions, such as Gaussian distributions, this algorithm reduces to the well-known expectation-maximization method. We prove global convergence of the algorithm under suitable assumptions, thus addressing an important open issue in the switching system identification literature. The effectiveness of both the proposed model and algorithm is validated through extensive numerical experiments.},

keywords = {},

pubstate = {published},

tppubtype = {workingpaper}

}