Papers
Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimization
Vinay Kanakeri, Shivam Bajaj, Ashwin Verma et al.
Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
Ruijie Du, Ruoyu Lin, Yanning Shen et al.
Deep QP Safety Filter: Model-free Learning for Reachability-based Safety Filter
Byeongjun Kim, H. Jin Kim
PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science
Nicholas C King, Jared Maeyama, Shubh Maheshwari et al.
Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis
Albert Lin, Alessandro Pinto, Somil Bansal
MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy
Saida Liu, Nikolay Atanasov, Shumon Koga
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems
Florian Courteville, Iain Henderson, Denis MATIGNON et al.
ATOM-CBF: Adaptive Safe Perception-Based Control under Out-of-Distribution Measurements
Kai S. Yun, Navid Azizan
Stability of Certainty-Equivalent Adaptive LQR for Linear Systems with Unknown Time-Varying Parameters
Marcell Bartos, Johannes Köhler, Florian Dorfler et al.
Data-driven Acceleration of MPC with Guarantees
Agustin Castellano, PAN SHIJIE, Enrique Mallada
Physics-Informed Neural Operators for Cardiac Electrophysiology
Hannah Lydon, Milad Kazemi, Martin Bishop et al.
Efficient State and Parameter Estimation of Nonlinear State-Space Models through Probabilistic Optimal Control
Victor Vantilborgh, Mohammad Mahmoudi Filabadi, Tom Lefebvre et al.
Provably Safe Stein Variational Clarity-Aware Informative Planning
Kaleb Ben Naveed, Utkrisht Sahai, Anouck Girard et al.
Policy Optimization for Unknown Systems using Differentiable MPC
Riccardo Zuliani, Efe C. Balta, John Lygeros
Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads
Eckart Cobo-Briesewitz, Khaled Wahba, Wolfgang Hönig
A Robust Task-Level Control Architecture for Learned Dynamical Systems
Eshika Pathak, Ahmed Aboudonia, Sandeep Banik et al.
Formalizing Task-Space Complexity for Zero-Shot Generalization
Jung-Hoon Cho, Heling Zhang, Siqi Du et al.
Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
Shafeef Omar, Majid Khadiv
TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations
Nikunj Gupta, Ludwika Twardecka, James Zachary Hare et al.
Optimal control of the future via prospective learning with control
Yuxin Bai, Aranyak Acharyya, Ashwin De Silva et al.
Sparse-to-Field Reconstruction via Stochastic Neural Dynamic Mode Decomposition
Yujin Kim, Sarah Dean
WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
Mintae Kim, Koushil Sreenath
Learning-Based Resilient Interval Observers for Nonlinear Discrete-Time Bounded-Error Systems
Mareddu Siva Rohit, Parisa Ansari Bonab, Elisabeth Andarge Gedefaw et al.
On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
Arthur Castello Branco de Oliveira, Dhruv D. Jatkar, Eduardo Sontag