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Found 125 papers, 22 papers with code
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Furthermore, as new data arrive, the belief over the atomic propositions evolves and, subsequently, the planning strategy adapts accordingly.
MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements
We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM.
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process
We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process.
Auto-Encoding Bayesian Inverse Games
When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling.
Online Foundation Model Selection in Robotics
We thus formulate a user-centric online model selection problem and propose a novel solution that combines an open-source encoder to output context and an online learning algorithm that processes this context.
Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine
Our method uses Large Language Models (LLM) to obtain high-level domain-specific knowledge using prompt engineering instead of providing the reinforcement learning algorithm directly with the high-level knowledge which requires an expert to encode the automaton.
News Source Credibility Assessment: A Reddit Case Study
In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation.
Zero-Shot Reinforcement Learning via Function Encoders
Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge.
Noise-Aware and Equitable Urban Air Traffic Management: An Optimization Approach
Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments.
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning
We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware.
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
Fine-Tuning Language Models Using Formal Methods Feedback
Although pre-trained language models encode generic knowledge beneficial for planning and control, they may fail to generate appropriate control policies for domain-specific tasks.
Algorithmic Robustness
Algorithmic robustness refers to the sustained performance of a computational system in the face of change in the nature of the environment in which that system operates or in the task that the system is meant to perform.
Encouraging Inferable Behavior for Autonomy: Repeated Bimatrix Stackelberg Games with Observations
As a converse result, we also provide a game where the required number of interactions is lower bounded by a function of the desired inferability loss.
Specification-Driven Video Search via Foundation Models and Formal Verification
The increasing abundance of video data enables users to search for events of interest, e. g., emergency incidents.
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data.
Verifiable Reinforcement Learning Systems via Compositionality
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Simulator-Driven Deceptive Control via Path Integral Approach
We study the deception problem in the continuous-state discrete-time stochastic dynamics setting and, using motivations from hypothesis testing theory, formulate a Kullback-Leibler control problem for the synthesis of deceptive policies.
Active Inverse Learning in Stackelberg Trajectory Games
We formulate an inverse learning problem in a Stackelberg game between a leader and a follower, where each player's action is the trajectory of a dynamical system.
Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception
We develop an algorithm that utilizes the knowledge from pretrained models to construct and verify controllers for sequential decision-making tasks, and to ground these controllers to task environments through visual observations.
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals.
Autonomous Drifting with 3 Minutes of Data via Learned Tire Models
Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled.
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks.
Risk-aware Urban Air Mobility Network Design with Overflow Redundancy
In our methodology, we first model how disruptions to a given UAM network might impact on the nominal traffic flow and how this flow might be re-accommodated on an extended network with reserve capacity.
Reinforcement Learning With Reward Machines in Stochastic Games
We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent.
Reward-Machine-Guided, Self-Paced Reinforcement Learning
Self-paced reinforcement learning (RL) aims to improve the data efficiency of learning by automatically creating sequences, namely curricula, of probability distributions over contexts.
Dynamic Routing in Stochastic Urban Air Mobility Networks: A Markov Decision Process Approach
Urban air mobility (UAM) is an emerging concept in short-range aviation transportation, where the aircraft will take off, land, and charge their batteries at a set of vertistops, and travel only through a set of flight corridors connecting these vertistops.
Efficient Sensitivity Analysis for Parametric Robust Markov Chains
As our main contribution, we present an efficient method to compute these partial derivatives.
Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learning
Markov games model interactions among multiple players in a stochastic, dynamic environment.
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples
Under no assumption of independent samples, we provide a high-probability, polynomial sample complexity bound for vanilla model-based off-policy evaluation that requires partial or uniform coverage.
Differential Privacy in Cooperative Multiagent Planning
We synthesize policies that are robust to privacy by reducing the value of the total correlation.
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem.
Task-Guided IRL in POMDPs that Scales
First, they require an excessive amount of data due to the information asymmetry between the expert and the learner.
Automaton-Based Representations of Task Knowledge from Generative Language Models
However, the textual outputs from GLMs cannot be formally verified or used for sequential decision-making.
Learning Temporal Logic Properties: an Overview of Two Recent Methods
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior.
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks
Toward the objective of learning composite models of such systems from data, we present i) a framework for compositional neural networks, ii) algorithms to train these models, iii) a method to compose the learned models, iv) theoretical results that bound the error of the resulting composite models, and v) a method to learn the composition itself, when it is not known a priori.
Sensor Placement for Online Fault Diagnosis
Fault diagnosis is the problem of determining a set of faulty system components that explain discrepancies between observed and expected behavior.
Differentially Private Timeseries Forecasts for Networked Control
We analyze a cost-minimization problem in which the controller relies on an imperfect timeseries forecast.
Online Poisoning Attacks Against Data-Driven Predictive Control
Data-driven predictive control (DPC) is a feedback control method for systems with unknown dynamics.
Learning Interpretable Temporal Properties from Positive Examples Only
To learn meaningful models from positive examples only, we design algorithms that rely on conciseness and language minimality of models as regularizers.
Categorical semantics of compositional reinforcement learning
However, generating compositional models requires the characterization of minimal assumptions for the robustness of the compositional feature.
Non-Parametric Neuro-Adaptive Formation Control
Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees.
Adversarial Examples for Model-Based Control: A Sensitivity Analysis
We propose a method to attack controllers that rely on external timeseries forecasts as task parameters.
Deceptive Planning for Resource Allocation
We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations.
Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning
We study the privacy risks that are associated with training a neural network's weights with self-supervised learning algorithms.
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics
We develop a reinforcement-learning algorithm that infers a reward machine that encodes the underlying task while learning how to execute it, despite the uncertainties of the propositions' truth values.
Safe Reinforcement Learning via Shielding under Partial Observability
Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment.
Safely: Safe Stochastic Motion Planning Under Constrained Sensing via Duality
We introduce the Safely motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot.
No-Regret Learning in Dynamic Stackelberg Games
The regret of the proposed learning algorithm is independent of the size of the state space and polynomial in the rest of the parameters of the game.
Class-Aware Adversarial Transformers for Medical Image Segmentation
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss
In this work, we develop joint policies for cooperative multiagent systems that are robust to potential losses in communication.
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data.
Non-Parametric Neuro-Adaptive Coordination of Multi-Agent Systems
Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees.
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models
Additionally, we study the effectiveness of two prominent mitigation methods for preempting MIAs, namely weight regularization and differential privacy.
Deceptive Decision-Making Under Uncertainty
We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments.
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
The physics-informed constraints are enforced via the augmented Lagrangian method during the model's training.
Simultaneous Perception-Action Design via Invariant Finite Belief Sets
Although perception is an increasingly dominant portion of the overall computational cost for autonomous systems, only a fraction of the information perceived is likely to be relevant to the current task.
On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning
We explore methodologies to improve the robustness of generative adversarial imitation learning (GAIL) algorithms to observation noise.
Convex Optimization for Parameter Synthesis in MDPs
The parameter synthesis problem is to compute an instantiation of these unspecified parameters such that the resulting MDP satisfies the temporal logic specification.
Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach
Specifically, we study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expressed in a recently-proposed language called graph temporal logic (GTL).
Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information
We develop a learning-based control algorithm for unknown dynamical systems under very severe data limitations.
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0. 5.
Ranked #19 on
Image Classification
on MNIST
(Accuracy metric)
Verifiable and Compositional Reinforcement Learning Systems
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Task-Guided Inverse Reinforcement Learning Under Partial Information
Nevertheless, the resulting formulation is still nonconvex due to the intrinsic nonconvexity of the so-called forward problem, i. e., computing an optimal policy given a reward function, in POMDPs.
Uncertainty-Aware Signal Temporal Logic Inference
In this paper, we first investigate the uncertainties associated with trajectories of a system and represent such uncertainties in the form of interval trajectories.
Safety-Constrained Learning and Control using Scarce Data and Reciprocal Barriers
We develop a control algorithm that ensures the safety, in terms of confinement in a set, of a system with unknown, 2nd-order nonlinear dynamics.
Efficient Strategy Synthesis for MDPs with Resource Constraints
We consider qualitative strategy synthesis for the formalism called consumption Markov decision processes.
Polynomial-Time Algorithms for Multi-Agent Minimal-Capacity Planning
We develop an algorithm that solves this graph problem in time that is \emph{polynomial} in the number of agents, target locations, and size of the consumption Markov decision process.
Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach
Our first algorithm infers minimal LTL formulas by reducing the inference problem to a problem in maximum satisfiability and then using off-the-shelf MaxSAT solvers to find a solution.
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specifications, similar to those required by search-and-rescue missions.
Self-Supervised Online Reward Shaping in Sparse-Reward Environments
We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in sparse-reward environments by automatically densifying rewards.
Generalization Bounds for Sparse Random Feature Expansions
In particular, we provide generalization bounds for functions in a certain class (that is dense in a reproducing kernel Hilbert space) depending on the number of samples and the distribution of features.
Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations
In such a setting, we develop a periodic transmission strategy, i. e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission.
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners
We then develop a kickstarted deep reinforcement learning algorithm for the student that is privacy-aware because we calibrate its objective with the parameters of the teacher's privacy mechanism.
On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems' Fundamental Lemma
Willems' fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and a persistency of excitation condition hold.
Safe Multi-Agent Reinforcement Learning via Shielding
Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e. g., no unsafe states are ever visited) during the learning process. Unfortunately, current MARL methods do not have safety guarantees.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multiple Plans are Better than One: Diverse Stochastic Planning
In planning problems, it is often challenging to fully model the desired specifications.
Faster Non-Convex Federated Learning via Global and Local Momentum
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of $\mathcal{O}(\epsilon^{-1. 5})$ to converge to an $\epsilon$-stationary point (i. e., $\mathbb{E}[\|\nabla f(\bm{x})\|^2] \leq \epsilon$) for smooth non-convex functions -- under arbitrary client heterogeneity and compressed communication -- compared to the $\mathcal{O}(\epsilon^{-2})$ complexity of most prior works.
Towards Online Monitoring and Data-driven Control: A Study of Segmentation Algorithms for Laser Powder Bed Fusion Processes
The identified algorithms can be readily applied to the laser powder bed fusion machines to address each of the above limitations and thus, significantly improve process control.
On-The-Fly Control of Unknown Systems: From Side Information to Performance Guarantees through Reachability
Besides, $\texttt{DaTaControl}$ achieves near-optimal control and is suitable for real-time control of such systems.
Assured Autonomy: Path Toward Living With Autonomous Systems We Can Trust
The second workshop held in February 2020, focused on existing capabilities, current research, and research trends that could address the challenges and problems identified in workshop.
On-The-Fly Control of Unknown Smooth Systems from Limited Data
We investigate the problem of data-driven, on-the-fly control of systems with unknown nonlinear dynamics where data from only a single finite-horizon trajectory and possibly side information on the dynamics are available.
Robust Finite-State Controllers for Uncertain POMDPs
(3) We linearize this dual problem and (4) solve the resulting finite linear program to obtain locally optimal solutions to the original problem.
Constrained Active Classification Using Partially Observable Markov Decision Processes
The proposed framework relies on assigning a classification belief (a probability distribution) to the attributes of interest.
Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology
We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively.
Near-Optimal Reactive Synthesis Incorporating Runtime Information
We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric.
On the Complexity of Sequential Incentive Design
We model the agent's behavior as a Markov decision process, express its intrinsic motivation as a reward function, which belongs to a finite set of possible reward functions, and consider the incentives as additional rewards offered to the agent.
Optimization and Control
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal.
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks
Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy.
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples
We compare our algorithm with the state-of-the-art RL algorithms for non-Markovian reward functions, such as Joint Inference of Reward machines and Policies for RL (JIRP), Learning Reward Machine (LRM), and Proximal Policy Optimization (PPO2).
Collaborative Beamforming Under Localization Errors: A Discrete Optimization Approach
In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold.
Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints
Recurrent neural networks (RNNs) have emerged as an effective representation of control policies in sequential decision-making problems.
Adaptive Teaching of Temporal Logic Formulas to Learners with Preferences
In the context of teaching temporal logic formulas, an exhaustive search even for a myopic solution takes exponential time (with respect to the time span of the task).
Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments.
Online Synthesis for Runtime Enforcement of Safety in Multi-Agent Systems
In this approach, which is fundamentally decentralized, the shield on every agent has two components: a pathfinder that corrects the behavior of the agent and an ordering mechanism that dynamically modifies the priority of the agent.
Online Active Perception for Partially Observable Markov Decision Processes with Limited Budget
In applications in which the agent does not have prior knowledge about the available information sources, it is crucial to synthesize active perception strategies at runtime.
Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach
We formulate the task of representation learning as that of mapping the state space of the model to a low-dimensional state space, called the kernel space.
Joint Inference of Reward Machines and Policies for Reinforcement Learning
The experiments show that learning high-level knowledge in the form of reward machines can lead to fast convergence to optimal policies in RL, while standard RL methods such as q-learning and hierarchical RL methods fail to converge to optimal policies after a substantial number of training steps in many tasks.
Transfer of Temporal Logic Formulas in Reinforcement Learning
Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL).
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion.
Synthesis of Provably Correct Autonomy Protocols for Shared Control
We design the autonomy protocol to ensure that the resulting robot behavior satisfies given safety and performance specifications in probabilistic temporal logic.
Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes
However, in a variety of real-world scenarios the agent has an active role in its perception by selecting which observations to receive.
Reward-Based Deception with Cognitive Bias
In this paper, we consider deceiving adversaries with bounded rationality and in terms of expected rewards.
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
The particular problem is to determine strategies that provably adhere to (probabilistic) temporal logic constraints.
Distributed Synthesis of Surveillance Strategies for Mobile Sensors
We study the problem of synthesizing strategies for a mobile sensor network to conduct surveillance in partnership with static alarm triggers.
Algorithms for Fairness in Sequential Decision Making
It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness.
Constrained Cross-Entropy Method for Safe Reinforcement Learning
We show that the asymptotic behavior of the proposed algorithm can be almost-surely described by that of an ordinary differential equation.
The Partially Observable Games We Play for Cyber Deception
Then, the deception problem is to compute a strategy for the deceiver that minimizes the expected cost of deception against all strategies of the infiltrator.
Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraints
Such a policy minimizes the predictability of the paths it generates, or dually, maximizes the exploration of different paths in an MDP while ensuring the satisfaction of a temporal logic specification.
Deception in Optimal Control
In this paper, we consider an adversarial scenario where one agent seeks to achieve an objective and its adversary seeks to learn the agent's intentions and prevent the agent from achieving its objective.
Counterexamples for Robotic Planning Explained in Structured Language
Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations.
Synthesis in pMDPs: A Tale of 1001 Parameters
This paper considers parametric Markov decision processes (pMDPs) whose transitions are equipped with affine functions over a finite set of parameters.
Verification of Markov Decision Processes with Risk-Sensitive Measures
We develop a method for computing policies in Markov decision processes with risk-sensitive measures subject to temporal logic constraints.
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes
The efficient verification of this MC gives quantitative insights into the quality of the inferred human strategy by proving or disproving given system specifications.
Sensor Synthesis for POMDPs with Reachability Objectives
Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors.
Control-Oriented Learning on the Fly
This paper focuses on developing a strategy for control of systems whose dynamics are almost entirely unknown.
Safe Reinforcement Learning via Shielding
In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions.
Strategy Synthesis in POMDPs via Game-Based Abstractions
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications.
Environment-Independent Task Specifications via GLTL
We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent.
Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction
Probabilistic model checking is used to predict the human's behavior.
Synthesis of Shared Control Protocols with Provable Safety and Performance Guarantees
We formalize synthesis of shared control protocols with correctness guarantees for temporal logic specifications.
An Automaton Learning Approach to Solving Safety Games over Infinite Graphs
We propose a method to construct finite-state reactive controllers for systems whose interactions with their adversarial environment are modeled by infinite-duration two-player games over (possibly) infinite graphs.
Correct-by-synthesis reinforcement learning with temporal logic constraints
We establish both correctness (with respect to the temporal logic specifications) and optimality (with respect to the a priori unknown performance criterion) of this two-step technique for a fragment of temporal logic specifications.
Integrating active sensing into reactive synthesis with temporal logic constraints under partial observations
We show that by alternating between the observation-based strategy and the active sensing strategy, under a mild technical assumption of the set of sensors in the system, the given temporal logic specification can be satisfied with probability 1.
Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraints
We model the interaction between the system and its environment as a Markov decision process (MDP) with initially unknown transition probabilities.
