yusuke1997/dblp-id-title huggingface dataset:
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search
Increasing information for model predictive control with semi-Markov decision processes
On the Robustness of Data-Driven Controllers for Linear Systems
Online switching control with stability and regret guarantees
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
Hybrid Modeling of Heterogeneous Human Teams for Collaborative Decision Processes
Finite Sample System Identification: Optimal Rates and the Role of Regularization
On task-relevant loss functions in meta-reinforcement learning
Optimal Cost Design for Model Predictive Control
Formal Synthesis of Safety Controllers for Unknown Stochastic Control Systems using Gaussian Process Learning
A Learning and Control Perspective for Microfinance
Uncertainty informed optimal resource allocation with Gaussian process based Bayesian inference
Uncertainty quantification of set-membership estimation in control and perception: Revisiting the minimum enclosing ellipsoid
A Generalizable Physics-informed Learning Framework for Risk Probability Estimation
Hierarchical Policy Blending As Optimal Transport
Sample Complexity of the Robust LQG Regulator with Coprime Factors Uncertainty
Training Lipschitz Continuous Operators Using Reproducing Kernels
TamedPUMA: safe and stable imitation learning with geometric fabrics
Learning nonlinear dynamical systems from a single trajectory
Robust Online Control with Model Misspecification
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-Reinforcement Learning
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
Proto-MPC: An encoder-prototype-decoder approach for quadrotor control in challenging winds
A data-driven Riccati equation
Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications
Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets
Towards safe multi-task Bayesian optimization
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach
Robust Reinforcement Learning: A Constrained Game-theoretic Approach
Probably approximately correct stability of allocations in uncertain coalitional games with private sampling
Nonconvex Linear System Identification with Minimal State Representation
Stable Reinforcement Learning with Unbounded State Space
Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control
Piecewise regression via mixed-integer programming for MPC
Learning to Actively Reduce Memory Requirements for Robot Control Tasks
A-NC: Adaptive Neural Control with implicit online inference of privileged parameters
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach
Compositional Neural Certificates for Networked Dynamical Systems
Topological State Space Inference for Dynamical Systems
Learning based attacks in Cyber Physical Systems: Exploration, Detection, and Control Cost trade-offs
Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Learning Convex Optimization Control Policies
Lipschitz constant estimation for 1D convolutional neural networks
Robust exploration with adversary via Langevin Monte Carlo
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives
Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator
Learning the Globally Optimal Distributed LQ Regulator
DLKoopman: A deep learning software package for Koopman theory
Diffeomorphic Transforms for Generalised Imitation Learning
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning
Learning Linear Complementarity Systems
Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands
On the Effectiveness of Iterative Learning Control
Robust Guarantees for Perception-Based Control
A learning-based framework to adapt legged robots on-the-fly to unexpected disturbances
Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
HSVI-based online minimax strategies for partially observable stochastic games with neural perception mechanisms
Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs
Symmetries-enhanced Multi-Agent Reinforcement Learning
Mixtures of Controlled Gaussian Processes for Dynamical Modeling of Deformable Objects
Tractable Reinforcement Learning of Signal Temporal Logic Objectives
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
Parameter-adaptive approximate MPC: Tuning neural-network controllers without retraining
Probabilistic robust linear quadratic regulators with Gaussian processes
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
Block Contextual MDPs for Continual Learning
Adaptive Model Predictive Control by Learning Classifiers
Robust error bounds for quantised and pruned neural networks
Convex approximations for a bi-level formulation of data-enabled predictive control
The Impact of Data on the Stability of Learning-Based Control
Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control
Data-driven bifurcation analysis via learning of homeomorphism
Traversing Time with Multi-Resolution Gaussian Process State-Space Models
A Data Driven, Convex Optimization Approach to Learning Koopman Operators
Operator Learning for Nonlinear Adaptive Control
State-wise safe reinforcement learning with pixel observations
Domain Adaptation Using System Invariant Dynamics Models
Learning Feasible Transitions for Efficient Contact Planning
Learning supported Model Predictive Control for Tracking of Periodic References
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
Data-driven Identification of Approximate Passive Linear Models for Nonlinear Systems
Data-driven design of switching reference governors for brake-by-wire applications
Regret Guarantees for Online Deep Control
The Complexity of Sequential Prediction in Dynamical Systems
STEMFold: Stochastic temporal manifold for multi-agent interactions in the presence of hidden agents
Improving Robustness via Risk Averse Distributional Reinforcement Learning
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
Objective Mismatch in Model-based Reinforcement Learning
Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective
Mixing classifiers to alleviate the accuracy-robustness trade-off
Minimax dual control with finite-dimensional information state
Model Predictive Control via On-Policy Imitation Learning
Time Varying Regression with Hidden Linear Dynamics
Structured Mechanical Models for Robot Learning and Control
Extended Convex Lifting for Policy Optimization of Optimal and Robust Control
Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems
Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms
Adaptive Conformal Prediction for Motion Planning among Dynamic Agents
Uncertainty quantification and robustification of model-based controllers using conformal prediction
Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions
Learning Recurrent Neural Net Models of Nonlinear Systems
Abstraction-Based Control of Unknown Continuous-Space Models with Just Two Trajectories
The Dynamics of Gradient Descent for Overparametrized Neural Networks
Opt-ODENet: Neural ODE Controller Design with Differentiable Optimization Layers for Safety and Stability
Adaptive Regret for Control of Time-Varying Dynamics
Probabilistic Invariance for Gaussian Process State Space Models
Learning Linear Models Using Distributed Iterative Hessian Sketching
Sample Complexity Bound for Evaluating the Robust Observer's Performance under Coprime Factors Uncertainty
Safe Decision Transformer with Learning-based Constraints
Accelerated Concurrent Learning Algorithms via Data-Driven Hybrid Dynamics and Nonsmooth ODEs
Distributionally Robust Lyapunov Function Search Under Uncertainty
Robust Regression for Safe Exploration in Control
A finite-sample analysis of multi-step temporal difference estimates
Flow matching for stochastic linear control systems
Generating Adversarial Disturbances for Controller Verification
On the uniqueness of solution for the Bellman equation of LTL objectives
ISAACS: Iterative Soft Adversarial Actor-Critic for Safety
Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees
Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space
A multi-modal distributed learning algorithm in reproducing kernel Hilbert spaces
Vision-based System Identification and 3D Keypoint Discovery using Dynamics Constraints
Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
Lagrangian inspired polynomial estimator for black-box learning and control of underactuated systems
Growing Q-networks: Solving continuous control tasks with adaptive control resolution
Continuous Versatile Jumping Using Learned Action Residuals
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
Approximate Midpoint Policy Iteration for Linear Quadratic Control
The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback
Regret-optimal measurement-feedback control
A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis
An invariant information geometric method for high-dimensional online optimization
Strengthened stability analysis of discrete-time Lurie systems involving ReLU neural networks
MyoSuite: A Contact-rich Simulation Suite for Musculoskeletal Motor Control
Regret Bounds for Adaptive Nonlinear Control
CT-DQN: Control-Tutored Deep Reinforcement Learning
Model-based Validation as Probabilistic Inference
Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping
Orthogonal projection-based regularization for efficient model augmentation
Linearised data-driven LSTM-based control of multi-input HVAC systems
A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time
Reinforcement Learning with Almost Sure Constraints
Exploiting Sparsity for Neural Network Verification
Do no harm: A counterfactual approach to safe reinforcement learning
Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system
Contraction ℒ 1 -Adaptive Control using Gaussian Processes
Suboptimal coverings for continuous spaces of control tasks
Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs
Convex neural network synthesis for robustness in the 1-norm
Neural operators for boundary stabilization of stop-and-go traffic
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
Contingency Constrained Planning with MPPI within MPPI
A deep learning approach for distributed aggregative optimization with users' feedback
Learning ε-Nash equilibrium stationary policies in stochastic games with unknown independent chains using online mirror descent
How safe am I given what I see? Calibrated prediction of safety chances for image-controlled autonomy
A Reinforcement Learning Look at Risk-Sensitive Linear Quadratic Gaussian Control
Certainty Equivalent Perception-Based Control
Robust Inverse Reinforcement Learning Control with Unknown States
LEOC: A Principled Method in Integrating Reinforcement Learning and Classical Control Theory
Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
Equilibria of Fully Decentralized Learning in Networked Systems
Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
α-RACER: Real-Time Algorithm for Game-Theoretic Motion Planning and Control in Autonomous Racing using Near-Potential Function
PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts
Symplectic Momentum Neural Networks - Using Discrete Variational Mechanics as a prior in Deep Learning
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Inverse optimal control as an errors-in-variables problem
The behavioral toolbox
Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning
Adaptive online non-stochastic control
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
Learning Distributed Channel Access Policies for Networked Estimation: Data-driven Optimization in the Mean-field Regime
Hyperparameter Tuning of an Off-Policy Reinforcement Learning Algorithm for H∞ Tracking Control
Nonlinear Data-Enabled Prediction and Control
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards
Feed-forward Neural Networks with Trainable Delay
Learning flow functions of spiking systems
Constrained Upper Confidence Reinforcement Learning
Sample Complexity of Kalman Filtering for Unknown Systems
Physics-Informed Model-Based Reinforcement Learning
Stable modular control via contraction theory for reinforcement learning
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees
Targeted Adversarial Attacks against Neural Network Trajectory Predictors
Safe Bayesian Optimisation for Controller Design by Utilising the Parameter Space Approach
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
Learning the Dynamics of Time Delay Systems with Trainable Delays
Multi-agent coverage control with transient behavior consideration
Expert with Clustering: Hierarchical Online Preference Learning Framework
Safe PDE Boundary Control with Neural Operators
QCQP-Net: Reliably learning feasible alternating current optimal power flow solutions under constraints
Riccati updates for online linear quadratic control
Real-time safe control of neural network dynamic models with sound approximation
SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions
Bayesian joint state and parameter tracking in autoregressive models
Interacting Particle Systems for Fast Linear Quadratic RL
Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling
Data-driven distributionally robust LQR with multiplicative noise
STLGame: Signal Temporal Logic Games in Adversarial Multi-Agent Systems
Learning How to Solve "Bubble Ball"
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
Predictive safety filter using system level synthesis
Learning Coherent Clusters in Weakly-Connected Network Systems
Learning locally interacting discrete dynamical systems: Towards data-efficient and scalable prediction
Optimal Algorithms for Submodular Maximization with Distributed Constraints
Scalable Natural Policy Gradient for General-Sum Linear Quadratic Games with Known Parameters
Direct Data-Driven Control with Embedded Anti-Windup Compensation
Generalized constraint for probabilistic safe reinforcement learning
Formation Shape Control using the Gromov-Wasserstein Metric
On the model-based stochastic value gradient for continuous reinforcement learning
Anytime Safe Reinforcement Learning
Multi-Task Imitation Learning for Linear Dynamical Systems
A large deviations perspective on policy gradient algorithms
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems
Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates
Virtual Reference Feedback Tuning with data-driven reference model selection
Adversarially Robust Stability Certificates can be Sample-Efficient
Policy Gradient Play with Networked Agents in Markov Potential Games
ValueNetQP: Learned One-step Optimal Control for Legged Locomotion
FedSysID: A Federated Approach to Sample-Efficient System Identification
Decision boundary learning for safe vision-based navigation via Hamilton-Jacobi reachability analysis and support vector machine
Safe Control with Neural Network Dynamic Models
Linear Stochastic Bandits over a Bit-Constrained Channel
Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution
WAVE: Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning
$\widetilde{O}(T^{-1})$ {C}onvergence to (coarse) correlated equilibria in full-information general-sum markov games
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
Wing shape estimation with Extended Kalman filtering and KalmanNet neural network of a flexible wing aircraft
Safe and Efficient Reinforcement Learning using Disturbance-Observer-Based Control Barrier Functions
A Dynamical Systems Perspective on Discrete Optimization
Signatures meet dynamic programming: Generalizing Bellman equations for trajectory following
Safe non-smooth black-box optimization with application to policy search
Faster Policy Learning with Continuous-Time Gradients
Data-Driven Abstraction of Monotone Systems
Distributed Neural Network Control with Dependability Guarantees: a Compositional Port-Hamiltonian Approach
Wasserstein distributionally robust regret-optimal control over infinite-horizon
Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems
Template-Based Piecewise Affine Regression
Federated Posterior Sharing for Multi-Agent Systems in Uncertain Environments
Improving Dynamic Regret in Distributed Online Mirror Descent Using Primal and Dual Information
PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings
Policy Optimization for H 2 Linear Control with H ∞ Robustness Guarantee: Implicit Regularization and Global Convergence
A Finite-Sample Deviation Bound for Stable Autoregressive Processes
Scalability Enhancement and Data-Heterogeneity Awareness in Gradient Tracking based Decentralized Bayesian Learning
Physics-penalised Regularisation for Learning Dynamics Models with Contact
Linear Antisymmetric Recurrent Neural Networks
Online Data Poisoning Attacks
Bayesian Learning with Adaptive Load Allocation Strategies
Online Saddle Point Tracking with Decision-Dependent Data
Uncertain-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric
Pontryagin neural operator for solving general-sum differential games with parametric state constraints
Informative Input Design for Dynamic Mode Decomposition
Lyapunov Perception Contracts for Operating Design Domains
NeurOpt: Neural network based optimization for building energy management and climate control
Design of observer-based finite-time control for inductively coupled power transfer system with random gain fluctuations
LiveNet: Robust, Minimally Invasive Multi-Robot Control for Safe and Live Navigation in Constrained Environments
Identifying Mechanical Models of Unknown Objects with Differentiable Physics Simulations
Lyceum: An efficient and scalable ecosystem for robot learning
Faster saddle-point optimization for solving large-scale Markov decision processes
L1-GP: L1 Adaptive Control with Bayesian Learning
Learning for Safety-Critical Control with Control Barrier Functions
A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
Learning for CasADi: Data-driven models in numerical optimization
Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching
Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
Efficient skill acquisition for insertion tasks in obstructed environments
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
Bridging Adaptivity and Safety: Learning Agile Collision-Free Locomotion Across Varied Physics
Offline Reinforcement Learning from Images with Latent Space Models
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
Combining model-based controller and ML advice via convex reparameterization
Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances
Learning the dynamics of autonomous nonlinear delay systems
On the nonsmooth geometry and neural approximation of the optimal value function of infinite-horizon pendulum swing-up
A Piecewise Learning Framework for Control of Unknown Nonlinear Systems with Stability Guarantees
Safe Reinforcement Learning with Chance-constrained Model Predictive Control
Probabilistic ODE solvers for integration error-aware numerical optimal control
Experience Replay with Likelihood-free Importance Weights
Tight sampling and discarding bounds for scenario programs with an arbitrary number of removed samples
DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization
Automating the loop in traffic incident management on highway
Learning Collective Dynamics of Multi-Agent Systems using Event-based Vision
Counterfactual Programming for Optimal Control
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information
Adaptive teaching in heterogeneous agents: Balancing surprise in sparse reward scenarios
Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models
On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds
Physics-constrained learning for PDE systems with uncertainty quantified port-Hamiltonian models
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Learning Approximate Forward Reachable Sets Using Separating Kernels
Analytical Integral Global Optimization
Noise Handling in Data-driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition
DC4L: Distribution shift recovery via data-driven control for deep learning models
Efficient imitation learning with conservative world models
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Multi-modal conformal prediction regions by optimizing convex shape templates
Graph Neural Networks for Distributed Linear-Quadratic Control
Towards model-free LQR control over rate-limited channels
Learning and steering game dynamics towards desirable outcomes