UW ARC LAB
Autonomous & Resilient Controls Lab

Publications

Below are the publications of ARC Lab after 2022. Xiangru’s previous publications can be found in his google scholar.

Refereed Journal Articles

  • Yujie Wang, Tianxiao Ye, Xiangru Xu, “Hierarchical Control for Occlusion-Free Visual Servoing of Robotic Manipulators”, IEEE Transactions on Control Systems Technology, 2026. [link] [pdf] [video] [code]
    [Abstract]
    Visual servoing leverages image features as feedback signals for robotic control, but existing methods generally lack rigorous formal guarantees for occlusion-free operation. This paper presents a hierarchical optimization-based visual servoing framework for robotic manipulators that ensures strict occlusion-free operation in continuous time. The framework integrates a high-level model predictive control with a low-level control barrier function-based controller. Occlusion-avoidance constraints are reformulated into differentiable convex constraints via a vertex-based image representation and a duality-based optimization approach, enabling seamless incorporation into the model predictive control. The control barrier functions are constructed from the relaxed minimum distance between polytopes, with distance derivatives computed using the KKT conditions of the distance problem. The resulting framework simultaneously achieves target feature-point regulation and continuous-time enforcement of occlusion-avoidance constraints. Hardware experiments on a Franka Research 3 manipulator validate the real-time implementability and strict occlusion avoidance of the proposed approach.
  • Hang Zhang, Xiangru Xu, “Safe Control Synthesis for Neural Network Control Systems via Constrained Zonotopes”, IEEE Control Systems Letters, 9: 3071-3076, 2025. (with presentation at the American Control Conference, New Orleans, LA, USA, 2026.) [link] [pdf] [code]
    [Abstract]
    This letter addresses the safe control synthesis problem for neural network control systems subject to bounded unknown disturbances and known exogenous inputs. A forward reachability analysis method is developed to over-approximate the system’s forward reachable sets using constrained zonotopes, where the control sequence appears linearly in both the zonotope center and the right-hand side of the associated equality constraints. Based on these over-approximations, a quadratically constrained program and its convexification are formulated to synthesize control sequences that guarantee safety. A numerical example demonstrates the effectiveness of the proposed approach.
  • Hang Zhang, Abigail J. Winn, Yuhao Zhang, Xiangru Xu, “Goal-Reaching Control Synthesis for Neural Network Control Systems via Backward Reachability”, IEEE Control Systems Letters, 9: 2771-2776, 2025.  (with presentation at the American Control Conference, New Orleans, LA, USA, 2026.) [link] [pdf] [code]
    [Abstract]
    This letter investigates goal-reaching control synthesis for neural network control systems. A backward reachability framework is developed based on constrained zonotopes, in which the graph set of a ReLU-activated feedforward neural network is encoded as a finite union of constrained zonotopes. Using this representation, under-approximations of backward reachable sets are computed for systems with nonlinear plant models, ensuring the feasibility of the goal-reaching task. Control sequences are then synthesized through an optimization procedure that exploits the under-approximated set. A numerical example demonstrates the effectiveness of the proposed approach.
  • Yujie Wang, Xiangru Xu, “Proxy Control Barrier Functions: Integrating Barrier-Based and Lyapunov-Based Safety-Critical Control Design”, 178: 112364, Automatica, 2025. [link] [pdf]
    [Abstract]
    This work introduces a novel Proxy Control Barrier Function (PCBF) scheme that integrates barrier-based and Lyapunov-based safety-critical control strategies for strict-feedback systems with potentially unknown dynamics. The proposed method employs a modular design procedure, decomposing the original system into a proxy subsystem and a virtual tracking subsystem that are controlled by the control barrier function (CBF)-based and Lyapunov-based controllers, respectively. By integrating these separately designed controllers, the overall system’s safety is ensured. Moreover, a new filter-based disturbance observer is utilized to design a PCBF-based safe controller for strict-feedback systems subject to mismatched disturbances. This approach broadens the class of systems to which CBF-based methods can be applied and significantly simplifies CBF construction by requiring only the model of the proxy subsystem. The effectiveness of the proposed method is demonstrated through numerical simulations.
  • Yuhao Zhang, Xiangru Xu, “Robust Stability of Neural Feedback Systems with Interval Matrix Uncertainties”, Automatica, 177: 112289, 2025. [link] [pdf]
    [Abstract]
    Neural networks have become increasingly popular in controller design due to their versatility and efficiency. However, their integration into feedback systems can pose stability challenges, particularly in the presence of uncertainties. This work addresses the problem of certifying robust stability in neural network control systems with interval matrix uncertainties. Leveraging classical robust stability techniques and the quadratic constraint-based method to characterize the input-output behavior of neural networks, we derive novel robust stability certificates formulated as linear matrix inequalities. To reduce computational complexity, we introduce three relaxed sufficient conditions and establish their equivalence in terms of feasibility. Additionally, we explore their connections to existing robust stability results. The effectiveness of the proposed approach is demonstrated through inverted pendulum and mass-spring-damper examples.
  • Shuoqi Wang, Keng-Yu Lin, Xiangru Xu, Michael Wehner, “A Holistic Indirect Contact Identification Method for Soft Robot Proprioception”, Soft Robotics, 12(5):578-592, 2025. [link] [pdf]
    [Abstract]
    Soft robots hold great promise but are notoriously difficult to control due to their compliance and backdrivability. In order to implement useful controllers, improved methods of perceiving robot pose (position and orientation of the entire robot body) in free and perturbed states are needed. In this work, we present a holistic approach to robot pose perception in free bending and with external contact, using multiple soft strain sensors on the robot (not collocated with the point of contact). By comparing the deviation of these sensors from their value in an unperturbed pose, we are able to perceive the mode and magnitude of deformation and thereby estimate the resulting perturbed pose of the soft actuator. We develop a sample 2 degree-of-freedom soft finger with two sensors, and we characterize sensor response to front, lateral, and twist deformation to perceive the mode and magnitude of external perturbation. We develop a data-driven model of free-bending deformation, we impose our perturbation perception method, and we demonstrate the ability to perceive perturbed pose on a single-finger and a two-finger gripper. Our holistic contact identification method provides a generalizable approach to perturbed pose perception needed for the control of soft robots.
  • Ziliang Lyu, Xiangru Xu, Yiguang Hong, Lihua Xie, “On Converse Zeroing Barrier Functions”, Automatica, 172: 112011, 2025. [link] [pdf]
    [Abstract]
    The paper studies the safety verification problem for nonlinear systems and focuses on the converse problem of zeroing barrier functions (ZBFs). We establish two necessary and sufficient conditions for the existence of a ZBF by solving the converse ZBF problem. Moreover, we also consider exponential barrier functions (EBFs), a special case of the ZBF, and provide a necessary and sufficient condition for the existence of an EBF when the state trajectory, starting from the interior of the safe set, cannot visit the boundary within finite time.
  • Yujie Wang, Xiangru Xu, “Immersion and Invariance-based Disturbance Observer and Its Application to Safe Control”, IEEE Transactions on Automatic Control, 69(12): 8782-8789, 2024. [link] [pdf]
    [Abstract]
    When the disturbance input matrix is nonlinear, existing disturbance observer design methods rely on the solvability of a partial differential equation or the existence of an output function with a uniformly well-defined disturbance relative degree, which can pose significant limitations. This note introduces a systematic approach for designing an immersion and invariance-based disturbance observer (IIDOB) that circumvents these strong assumptions. The proposed IIDOB ensures the disturbance estimation error is globally uniformly ultimately bounded by approximately solving a partial differential equation while compensating for the approximation error. Furthermore, by integrating IIDOB into the framework of control barrier functions, a filter-based safe control design method for control-affine systems with disturbances is established, where the filter is used to generate an alternative disturbance estimation signal with a known derivative. Sufficient conditions are established to guarantee the safety of the disturbed systems. Simulation results demonstrate the effectiveness of the proposed method.
  • Yuhao Zhang, Hang Zhang, Xiangru Xu, “Reachability Analysis of Neural Network Control Systems with Tunable Accuracy and Efficiency”, IEEE Control Systems Letters, 8: 1697-1702, 2024. (with presentation at the IEEE Conference on Decision and Control, Milan, Italy, 2024.) [link] [pdf]
    [Abstract]
    The proliferation of neural networks in safety-critical applications necessitates the development of effective methods to ensure their safety. This letter presents a novel approach for computing the exact backward reachable sets of neural feedback systems with known linear system models based on hybrid zonotopes. It is shown that the input-output relationship imposed by a ReLU-activated neural network can be exactly described by a hybrid zonotope-represented graph set. Based on that, the one-step exact backward reachable set of a neural feedback system is computed as a hybrid zonotope in the closed form. In addition, a necessary and sufficient condition is formulated as a mixed-integer linear program to certify whether the trajectories of a neural feedback system can avoid unsafe regions in finite time. Numerical examples are provided to demonstrate the efficiency of the proposed approach.
  • Yujie Wang, Xiangru Xu, “Adaptive Safety-Critical Control for a Class of Nonlinear Systems with Parametric Uncertainties: A Control Barrier Function Approach”, Systems & Control Letters, 188: 105798, 2024. [link] [pdf]
    [Abstract]
    This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control-input matrices. The method combines control barrier functions and adaptive laws to generate a safe controller through a nonlinear program with an explicitly given closed-form solution. The proposed approach verifies the non-emptiness of the admissible control set independently of online parameter estimations, which can ensure that the safe controller is singularity-free. A data-driven algorithm is also developed to improve the performance of the proposed controller by tightening the bounds of the unknown parameters. The effectiveness of the control scheme is demonstrated through numerical simulations.
  • Ziliang Lyu, Xiangru Xu, Yiguang Hong, “Small-Gain Theorem for Safety Verification under High-Relative-Degree Constraints”, IEEE Transactions on Automatic Control, 69(6): 3717 – 3731, 2024. [link] [pdf] 
    [Abstract]
    This article develops a small-gain technique for the safety analysis and verification of interconnected systems with high-relative-degree safety constraints. To this end, a high-relative-degree input-to-state safety (ISSf) approach is proposed to quantify the influence of external inputs on the subsystem safety. With a coordination transformation, the relationship between ISSf barrier functions (ISSf-BFs) and the existing high-relative-degree (or high-order) barrier functions is established to simplify the safety analysis under external inputs. With high-relative-degree ISSf-BFs, a small-gain theorem is proposed for safety verification. It is shown that, under the small-gain condition, the compositional safe set is forward invariant and asymptotically stable. The effectiveness of the proposed small-gain theorem is illustrated on the output-constrained decentralized control of two inverted pendulums connected by a spring mounted on two carts.
  • Yuhao Zhang, Hang Zhang, Xiangru Xu, “Backward Reachability Analysis of Neural Feedback Systems Using Hybrid Zonotopes”, IEEE Control Systems Letters, 7: 2779-2784, 2023. (with presentation at the IEEE Conference on Decision and Control, Marina Bay Sands, Singapore, 2023.) [link] [pdf]
    [Abstract]
    The proliferation of neural networks in safety-critical applications necessitates the development of effective methods to ensure their safety. This letter presents a novel approach for computing the exact backward reachable sets of neural feedback systems with known linear system models based on hybrid zonotopes. It is shown that the input-output relationship imposed by a ReLU-activated neural network can be exactly described by a hybrid zonotope-represented graph set. Based on that, the one-step exact backward reachable set of a neural feedback system is computed as a hybrid zonotope in the closed form. In addition, a necessary and sufficient condition is formulated as a mixed-integer linear program to certify whether the trajectories of a neural feedback system can avoid unsafe regions in finite time. Numerical examples are provided to demonstrate the efficiency of the proposed approach.
  • Victor Freire, Xiangru Xu, “Flatness-based Quadcopter Trajectory Planning and Tracking with Continuous-time Safety Guarantees”, IEEE Transactions on Control Systems Technology, 31(6): 2319–2334, 2023. [link] [pdf] [video] [code]
    [Abstract]
    This work proposes a convex optimization-based framework for the trajectory planning and tracking of quadcopters that ensures continuous-time safety guarantees. Using the convexity property of B-spline curves and the differential flatness property of quadcopters, a second-order cone program is formulated to generate an optimal nominal trajectory that respects state and input constraints, including position, linear velocity, angle, angular velocity, thrust, waypoints, and obstacle avoidance constraints, rigorously in the continuous-time sense. To ensure safe trajectory tracking, a convex quadratic program is proposed based on control barrier functions, which guarantees that the actual trajectory of the quadcopter remains within a prescribed safe tube of the nominal trajectory in continuous time. Furthermore, conditions that ensure the safe tracking controller respects thrust, roll, and pitch constraints are also presented. Both the planning and control approaches are suitable for online implementation, and the effectiveness of the proposed framework is demonstrated through simulations and experiments with a Crazyflie2.1 nano quadcopter.
  • Victor Freire, Xiangru Xu, “Optimal Control for Kinematic Bicycle Model with Continuous-time Safety Guarantees: A Sequential Second-order Cone Programming Approach”, IEEE Robotics and Automation Letters, 7(4): 11681-11688, 2022. [link] [pdf] [code]  
    [Abstract]
    The optimal control problem for the kinematic bicycle model is considered where the trajectories are required to satisfy the safety constraints in the continuous-time sense. Based on the differential flatness property of the model, necessary and sufficient conditions in the flat space are provided to guarantee safety in the state space. The optimal control problem is relaxed into three second-order cone programs (SOCPs) solved sequentially, which find the safe path, the trajectory duration, and the speed profile, respectively. Solutions of the three SOCPs provide a sub-optimal but feasible trajectory in the original optimal control problem. Simulation examples and comparisons with state-of-the-art optimal control solvers are presented to demonstrate the effectiveness of the proposed approach.
  • Ziliang Lyu, Xiangru Xu, Yiguang Hong, “Small-Gain Theorem for Safety Verification of Interconnected Systems”, Automatica, 139: 110178, 2022. [link] [pdf]
    [Abstract]
    A small-gain theorem in the formulation of barrier function is developed in this work for safety verification of interconnected systems. This result is helpful to verify input-to-state safety (ISSf) for interconnected systems from the safety information encoded in the individual ISSf-barrier functions. Also, it can be used to obtain a safety set in a higher dimensional space from the safety sets in two lower dimensional spaces.

Refereed Conference Papers

  • Yuhao Zhang, Xiangru Xu, “Forward and Backward Reachability Analysis of Closed-loop Recurrent Neural Networks via Hybrid Zonotopes”, American Control Conference, New Orleans, LA, USA, 2026. (accepted) [arxiv] [code]  
    [Abstract]
    Recurrent neural networks (RNNs) are widely employed to model complex dynamical systems due to their hidden-state structure, which inherently captures temporal dependencies. This work presents a hybrid zonotope-based approach for computing exact forward and backward reachable sets of closed-loop RNN systems with ReLU activation functions. The method formulates state-pair sets to compute reachable sets as hybrid zonotopes without requiring unrolling. To improve scalability, a tunable relaxation scheme is proposed that ranks unstable ReLU units across all layers using a triangle-area score and selectively applies convex relaxations within a fixed binary limit in the hybrid zonotopes. This scheme enables an explicit tradeoff between computational complexity and approximation accuracy, with exact reachability as a special case. In addition, a sufficient condition is derived to certify the safety of closed-loop RNN systems. Numerical examples demonstrate the effectiveness of the proposed approach.
  • Hang Zhang, Harry Zhang, Yujie Wang, Zhenhao Zhou, Dan Negrut, Xiangru Xu, “Shared Control of Teleoperated Vehicles with Delay-Compensated Safety Filtering”, IEEE Conference on Control Technology and Applications, San Diego, CA, USA, page 192-199, 2025. [link] [pdf] 
    [Abstract]
    This work presents a new shared control framework for teleoperated vehicles, targeting critical safety challenges arising from the control communication latency and the correctness of driver warnings. The proposed delaycompensated shared control architecture integrates two key components: a conformal prediction-based warning system that proactively alerts remote drivers of potential hazards and an onboard safety filter that combines a delay compensator, a disturbance observer, and a control barrier function-based quadratic program. The proposed design framework generates real-time safe control commands at the human-operation level despite delayed human inputs. A high-fidelity simulation platform was developed for semi-autonomous vehicle teleoperation using Chrono, a multi-physics-based simulator. Through extensive experiments in diverse scenarios, the proposed approach demonstrates robust performance and reliable safety maintenance under aggressive maneuvers and communication delays.
  • Yuhao Zhang, Xiangru Xu, “Efficient Reachability Analysis for Convolutional Neural Networks Using Hybrid Zonotopes”, American Control Conference, Denver, Colorado, USA, page 1321-1326, 2025. [link] [pdf] [code] 
    [Abstract]
    Feedforward neural networks are widely used in autonomous systems, particularly for control and perception tasks within the system loop. However, their vulnerability to adversarial attacks necessitates formal verification before deployment in safety-critical applications. Existing set propagation-based reachability analysis methods for feedforward neural networks often struggle to achieve both scalability and accuracy. This work presents a novel set-based approach for computing the reachable sets of convolutional neural networks. The proposed method leverages a hybrid zonotope representation and an efficient neural network reduction technique, providing a flexible trade-off between computational complexity and approximation accuracy. Numerical examples are presented to demonstrate the effectiveness of the proposed approach.
  • Hang Zhang, Yuhao Zhang, Xiangru Xu, “Hybrid Zonotope-Based Backward Reachability Analysis for Neural Feedback Systems With Nonlinear Plant Models”, American Control Conference, Toronto, Canada, page 4140-4146, 2024. [link] [pdf]
    [Abstract]
    The increasing prevalence of neural networks in safety-critical control systems underscores the imperative need for rigorous methods to ensure the reliability and safety of these systems. This work introduces a novel approach employing hybrid zonotopes to compute the over-approximation of backward reachable sets for neural feedback systems with non-linear plant models and general activation functions. Closed-form expressions as hybrid zonotopes are provided for the over-approximated backward reachable sets, and a refinement procedure is proposed to alleviate the potential conservatism of the approximation. Two numerical examples are provided to illustrate the effectiveness of the proposed approach.
  • Sriram Ashokkumar, et al, “Rapid Development of an Autonomous Vehicle for the SAE AutoDrive Challenge II Competition”, WCX SAE World Congress Experience, Detroit, Michigan, USA, 2024-01-1980, 2024. [link]
    [Abstract]
    The SAE AutoDrive Challenge II is a four-year collegiate competition dedicated to developing a Level 4 autonomous vehicle by 2025. In January 2023, the participating teams each received a Chevy Bolt EUV. Within a span of five months, the second phase of the competition took place in Ann Arbor, MI. The authors of this contribution, who participated in this event as team Wisconsin Autonomous representing the University of Wisconsin–Madison, secured second place in static events and third place in dynamic events. This has been accomplished by reducing reliance on the actual vehicle platform and instead leveraging physical analogs and simulation. This paper outlines the software and hardware infrastructure of the competing vehicle, touching on issues pertaining sensors, hardware, and the software architecture employed on the autonomous vehicle. We discuss the LiDAR-camera fusion approach for object detection and the three-tier route planning and following systems. One of the defining aspects of our approach was leveraging early simulation and the use of physical analogs, which accelerated the development of the autonomy algorithms. In the process, we established a rapid autonomous vehicle development methodology that will anchor our technical effort in the third stage of the SAE AutoDrive Challenge II competition.
  • Yujie Wang, Xiangru Xu, “Safe Control of Euler-Lagrange Systems with Limited Model Information”, IEEE Conference on Decision and Control, Marina Bay Sands, Singapore, page 5722-5728, 2023. [link] [pdf]
    [Abstract]
    This work presents a new safe control framework for Euler-Lagrange (EL) systems with limited model information, external disturbances, and measurement uncertainties. The EL system is decomposed into two subsystems called the proxy subsystem and the virtual tracking subsystem. An adaptive safe controller based on barrier Lyapunov functions is designed for the virtual tracking subsystem to ensure the boundedness of the safe velocity tracking error, and a safe controller based on control barrier functions is designed for the proxy subsystem to ensure controlled invariance of the safe set defined either in the joint space or task space. Theorems that guarantee the safety of the proposed controllers are provided. In contrast to existing safe control strategies for EL systems, the proposed method requires much less model information and can ensure safety rather than input-to-state safety. Simulation results are provided to illustrate the effectiveness of the proposed method.
  • Yuhao Zhang, Xiangru Xu, “Reachability Analysis and Safety Verification of Neural Feedback Systems via Hybrid Zonotopes”, American Control Conference,  San Diego, California, USA, page 1915-1921, 2023. [link] [pdf]
    [Abstract]
    Hybrid zonotopes generalize constrained zonotopes by introducing additional binary variables and possess some unique properties that make them convenient to represent nonconvex sets. This paper presents novel hybrid zonotope-based methods for the reachability analysis and safety verification of neural feedback systems. Algorithms are proposed to compute the input-output relationship of each layer of a feed-forward neural network, as well as the exact reachable sets of neural feedback systems. It is shown that a ReLU-activated feed-forward neural network can be exactly represented by a hybrid zonotope. In addition, a sufficient and necessary condition is formulated as a mixed-integer linear program to certify whether the trajectories of a neural feedback system can avoid unsafe regions. The proposed approach is shown to yield a formulation that provides the tightest convex relaxation for the reachable sets of the neural feedback system. Complexity reduction techniques for the reachable sets are developed to balance the computation efficiency and approximation accuracy. Two numerical examples demonstrate the superior performance of the proposed approach compared to other existing methods.
  • Yujie Wang, Xiangru Xu, “Disturbance Observer-based Robust Control Barrier Functions”, American Control Conference, San Diego, California, USA, page 3681-3687, 2023. [link] [pdf]
    [Abstract]
    This work presents a safe control design approach that integrates the disturbance observer (DOB) and the control barrier function (CBF) for systems with external disturbances. Different from existing robust CBF results that consider the “worst case” of disturbances, this work utilizes a DOB to estimate and compensate for the disturbances. DOB-CBF-based controllers are constructed with provably safe guarantees by solving convex quadratic programs online, to achieve a better tradeoff between safety and performance. Two types of systems are considered individually depending on the magnitude of the input and disturbance relative degrees. The effectiveness of the proposed methods is illustrated via numerical simulations.
  • Yuhao Zhang, Xiangru Xu, “Safety Verification of Neural Feedback Systems Based on Constrained Zonotopes”, IEEE Conference on Decision and Control, Cancun, Mexico, page 2737-2744, 2022. [link] [pdf]
    [Abstract]
    Artificial neural networks have recently been utilized in many feedback control systems and introduced new challenges regarding the safety of such systems. This paper considers the safe verification problem for a dynamical system with a given feedforward neural network as the feedback controller by using a constrained zonotope-based approach. A novel set-based method is proposed to compute both exact and over-approximated reachable sets for neural feedback systems with linear models, and linear program-based sufficient conditions are presented to verify whether the trajectories of such a system can avoid unsafe regions represented as constrained zonotopes. The results are also extended to neural feedback systems with nonlinear models. The computational efficiency and accuracy of the proposed method are demonstrated by two numerical examples where a comparison with state-of-the-art methods is also provided.
  • Yuhao Zhang, Sequoyah Walters, Xiangru Xu, “Control Barrier Function Meets Interval Analysis: Safety-Critical Control with Measurement and Actuation Uncertainties”, American Control Conference, Atlanta, Georgia, USA, page 3814–3819, 2022. [link] [pdf] [code]
    [Abstract]
    This paper presents a framework for designing provably safe feedback controllers for sampled-data control affine systems with measurement and actuation uncertainties. Based on the interval Taylor model of nonlinear functions, a sampled-data control barrier function (CBF) condition is proposed which ensures the forward invariance of a safe set for sampled-data systems. Reachable set overapproximation and Lasserre’s hierarchy of polynomial optimization are used for finding a margin term in the sampled-data CBF condition. Sufficient conditions for a safe controller in the presence of measurement and actuation uncertainties are proposed. The effectiveness of the proposed method is illustrated by a numerical example and an experimental example that implements the proposed controller on the Crazyflie quadcopter in real-time.
  • Yujie Wang, Xiangru Xu, “Observer-based Control Barrier Functions for Safety Critical Systems”, American Control Conference, Atlanta, Georgia, USA, page 709–714, 2022. [link] [pdf]
    [Abstract]
    This paper considers the safety-critical control design problem with output measurements. An observer-based safety control framework that integrates the estimation error quantified observer and the control barrier function (CBF) approach is proposed. The function approximation technique is employed to approximate the uncertainties introduced by the state estimation error, and an adaptive CBF approach is proposed to design the safe controller which is obtained by solving a convex quadratic program (QP). Theoretical results for CBFs with a relative degree 1 and a higher relative degree are given individually. The effectiveness of the proposed control approach is demonstrated by two numerical examples.

Technical Reports

  • Harry Zhang, Stefan Caldararu, et al, “Using simulation to design an MPC policy for field navigation using GPS sensing”, ECCOMAS Thematic Conference on Multibody Dynamics, Lisbon, Portugal, 2023. [pdf]
  • Harry Zhang, Stefan Caldararu, et al, “A Case Study of the Sim-to-Real Gap When Designing PID and MPC Controllers in Simulation”, ECCOMAS Thematic Conference on Multibody Dynamics, Lisbon, Portugal, 2023. (Extended Abstract) [pdf]
  • Asher Elmquist, Aaron Young, et al, “ART/ATK: A research platform for assessing and mitigating the sim-to-real gap in robotics and autonomous vehicle engineering”, IROS Workshop on Miniature Robot Platforms for Full Scale Autonomous Vehicle Research, 2022. [pdf]
  • Asher Elmquist, Aaron Young, et al, “A software toolkit and hardware platform for investigating and comparing robot autonomy algorithms in simulation and reality”, 2022. [pdf]