Junbo Tan
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View article: Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption
Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption Open
Pretraining a policy on offline data followed by fine-tuning through online interactions, known as Offline-to-Online Reinforcement Learning (O2O RL), has emerged as a promising paradigm for real-world RL deployment. However, both offline d…
View article: Data-Driven MPC with Data Selection for Flexible Cable-Driven Robotic Arms
Data-Driven MPC with Data Selection for Flexible Cable-Driven Robotic Arms Open
Flexible cable-driven robotic arms (FCRAs) offer dexterous and compliant motion. Still, the inherent properties of cables, such as resilience, hysteresis, and friction, often lead to particular difficulties in modeling and control. This pa…
View article: A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning
A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning Open
Autonomous navigation of vehicle-trailer systems is crucial in environments like airports, supermarkets, and concert venues, where various types of trailers are needed to navigate with different payloads and conditions. However, accurately…
View article: Unlocking adaptive digital pathology through dynamic feature learning
Unlocking adaptive digital pathology through dynamic feature learning Open
Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quantitative analysis of critical histological patterns and the d…
View article: AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization Open
Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which learns the value function using only dataset actions through quantile regression. However, it is unclear how to recover the implicit policy from the learned implic…
View article: Offline Goal-Conditioned Reinforcement Learning for Safety-Critical Tasks with Recovery Policy
Offline Goal-Conditioned Reinforcement Learning for Safety-Critical Tasks with Recovery Policy Open
Offline goal-conditioned reinforcement learning (GCRL) aims at solving goal-reaching tasks with sparse rewards from an offline dataset. While prior work has demonstrated various approaches for agents to learn near-optimal policies, these m…
View article: DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning Open
Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR). However, previous methods may still encounter out-of-distribution actions due to …
View article: Visuotactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling
Visuotactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling Open
Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuo-tactile sensor and the pneumatic actuator-based gri…
View article: Data-Driven Robust Control for Discrete Linear Time-Invariant Systems: A Descriptor System Approach
Data-Driven Robust Control for Discrete Linear Time-Invariant Systems: A Descriptor System Approach Open
Given the recent surge of interest in data-driven control, this paper proposes a two-step method to study robust data-driven control for a parameter-unknown linear time-invariant (LTI) system that is affected by energy-bounded noises. Firs…
View article: A Novel Method Designing Optimal Observer Gain for Robust Fault Detection
A Novel Method Designing Optimal Observer Gain for Robust Fault Detection Open
This paper considers the design of optimal gain of set-value observer for robust fault detection (FD) from a new perspective. Set-based robust FD can be implemented by real-timely check whether the measured output is contained in the corre…
View article: Active Fault-Tolerant Control Framework for Linear Parameter-Varying Systems Affected by Sensor Faults
Active Fault-Tolerant Control Framework for Linear Parameter-Varying Systems Affected by Sensor Faults Open
In this paper, we propose a novel fault tolerant multi-sensor switching control framework for linear parameter-varying systems considering multiplicative and additive sensor faults simultaneously. We show that the closed-loop stability is …
View article: Optimal robust fault detection of discrete‐time LPV systems with measurement error‐affected scheduling variables combining ZKF and pQP
Optimal robust fault detection of discrete‐time LPV systems with measurement error‐affected scheduling variables combining ZKF and pQP Open
Summary Optimal robust state estimation (SE) and fault detection (FD) methods of discrete‐time linear parameter varying systems with measurement error‐affected scheduling variables are proposed under the boundedness assumption of system un…
View article: Multiple Multiplicative Actuator Fault Detectability Analysis Based on Invariant Sets for Discrete-time LPV Systems
Multiple Multiplicative Actuator Fault Detectability Analysis Based on Invariant Sets for Discrete-time LPV Systems Open
This paper proposes a generalized minimum detectable fault (MDF) computation method based on the set-separation condition between the healthy and faulty residual sets for discrete-time linear parameter varying (LPV) systems with bounded in…
View article: Robust Fault Detection and Isolation of Discrete-Time LPV Systems Combining Set-theoretic UIO and Invariant Sets
Robust Fault Detection and Isolation of Discrete-Time LPV Systems Combining Set-theoretic UIO and Invariant Sets Open
This paper proposes a mixed active/passive robust fault detection and isolation (FDI) method for discrete-time linear paramter varying (LPV) systems based on set-theoretic unknown input observers (SUIO) and invariant sets. The robustness a…
View article: Invariant Set-Based Analysis of Minimal Detectable Fault for Discrete-Time LPV Systems With Bounded Uncertainties
Invariant Set-Based Analysis of Minimal Detectable Fault for Discrete-Time LPV Systems With Bounded Uncertainties Open
This paper proposes an invariant-set based minimal detectable fault (MDF) computation method based on the set-separation condition between the healthy and faulty residual sets for discrete-time linear parameter varying (LPV) systems with b…
View article: A Fast Method for Multi-Objective Nonlinear Dynamics Optimization of a Storage Ring
A Fast Method for Multi-Objective Nonlinear Dynamics Optimization of a Storage Ring Open
Multi-objective evolutionary algorithms (MOEAs), including multi-objective genetic algorithm and particle swarm optimization algorithm, have been widely applied in the nonlinear dynamics optimization of storage ring light sources. In the o…
View article: Tube-based Robust Fault Estimation Integrating Unknown Inputs Decoupling and Set-membership Approach
Tube-based Robust Fault Estimation Integrating Unknown Inputs Decoupling and Set-membership Approach Open
This paper proposes a novel tube-based robust fault estimation (FE) method for dynamic systems by decoupling unknown inputs and using set-membership approach. FE problems are considered in a novel prospective in this paper. Instead of esti…
View article: Generalized set‐theoretic unknown input observer for LPV systems with application to state estimation and robust fault detection
Generalized set‐theoretic unknown input observer for LPV systems with application to state estimation and robust fault detection Open
Summary This paper proposes to design an unknown input observer (UIO) for the linear‐parameter‐varying (LPV) system on the basis of the set theory, which is named as the set‐theoretic UIO (SUIO). The advantage of the SUIO consists in that …
View article: Robust state estimation and fault detection combining unknown input observer and set-membership approach
Robust state estimation and fault detection combining unknown input observer and set-membership approach Open
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View article: A novel design of unknown input observers using set-theoretic methods for robust fault detection
A novel design of unknown input observers using set-theoretic methods for robust fault detection Open
This paper proposes a novel unknown input observer (UIO) design method, which incorporates the settheoretic notions into the design of UIOs. In this way, we can take advantage of both UIOs and set-theoretic methods in fault detection (FD).…