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Robust Dynamic Periodic Event-triggered Control of Lur’e Systems
No full textThis paper addresses the robust stabilization of continuous-time uncertain Lur'e systems with dynamic periodic event-triggered control. Based on Lyapunov arguments and the looped-functional technique, we propose constructive conditions expressed in the form of linear matrix inequalities to globally stabilize the origin of the closed-loop system. These conditions are cast into a convex optimization problem to systematically co-design the control law gains and the triggering parameters aiming at reducing the number of control updates. A numerical example illustrates the proposed method.</div
A Whole-Body Multi Contact Large Object Manipulation and Estimation Framework for Humanoids Using Skin Patches
No full textInternational audienceOver the years, robotic manipulation has primarily focused on end-effectors, an approach that severely limits a robot's ability to manipulate large and heavy objects. Humanoids, which are expected to operate in human environments, must acquire this skill to enhance their versatility and usefulness. In this regard, we present a whole-body multicontact manipulation (WBMC) framework to handle large and heavy objects. To facilitate WBMC, we incorporate artificial skin patches distributed across the humanoid's upper body which are effectively utilized for contact detection and force sensing. The WBMC manipulation problem is formulated as an optimal control problem (OCP) within a model predictive control (MPC) framework, and three different types of dynamic motions are used to evaluate the controller's effectiveness. The proposed framework manages the entire manipulation process, including reaching, grasping, picking up, and manipulating. Furthermore, these motions are leveraged to develop a twostage object inertial parameter estimation framework. The first stage estimates the object's mass and center of mass, while the second estimates its inertia. Both the manipulation and estimation frameworks are numerically evaluated using the TALOS humanoid and a rectangular box in simulation, and their respective results are presented and discussed
Optimal Control of State Constrained Systems via Measure Relaxations and Polynomial Optimization
No full textInternational audienceWe address the optimal control problem for a class of dynamical systems with constrained state trajectories. These systems are modeled by a differential inclusion with a drift term and a normal cone mapping associated with the constraint set. The optimal control problem is considered in continuous-time and discrete-time, where the latter provides a computational advantage over the former. In both cases, the nonlinear problem is reformulated as an infinite-dimensional linear program over occupation measures. We show that this does not introduce any relaxation gap, that is, the optimal value remains the same for the reformulated linear program. Using appropriate tools from functional analysis and optimal transport, we also show the convergence of the optimal value of the discrete problem to the optimal value of the continuous problem. We propose finite-dimensional convex optimization algorithms based on the moment-sum-of-squares hierarchy to provide numerical approximations of the proposed infinite-dimensional linear programs
A Complex Network Analysis Approach for Generating Realistic Instances of the Scheduled Service Network Design Problem
No full textLong-haul freight transportation forms the backbone of global supply chains and involves diverse types of carriers, e.g., liner shipping companies, rail freight operators, less-than-truckload carriers, express parcel companies, etc. Planning decisions across these applications can be assisted by solving the Scheduled Service Network Design Problem, a fundamental but computationally challenging optimization problem. However, most instances used in the literature are not accessible, and the few available benchmarks were not designed to reflect the structural properties of real-world freight transportation networks. To fill this gap, we introduce a new open-source generator of instances for the Scheduled Service Network Design Problem. Our generator leverages insights from Complex Network Analysis to reproduce key structural features of freight transportation networks, while allowing users to tune parameters to generate diversified instances. A computational study validates its ability to produce networks exhibiting metrics aligned with those of networks derived from existing freight applications. We also include features such as preprocessing rules, varied demand pattern generation, and network emulation to increase the value of our tool for both practitioners and researchers
On the instability of local learning algorithms: Q-learning can fail in infinite state spaces
No full textWe investigate the challenges of applying model-free reinforcement learning algorithms, like online Q-learning, to infinite state space Markov Decision Processes (MDPs). We first introduce the notion of Local Learning Processes (LLPs), where agents make decisions based solely on local information, and we show that Q-learning can be seen as a specific instance of an LLP. Using renewal techniques, we analyze LLPs and demonstrate their instability under certain drift and initial conditions, revealing fundamental limitations in infinite state spaces. In particular, we show that while asymptotically optimal in finite settings, Q-learning can face instability and strict sub-optimality in infinite spaces. Our findings are illustrated through queueing system examples drawn from load balancing and server allocation. The study underscores the need for new theoretical frameworks and suggests future research into nonlocal Q-learning variants
Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring
No full textModel Predictive Control (MPC) is widely used for torque-controlled robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industrial tasks under collision constraints. Deburring in particular requires precise tool insertion, stable force regulation, and collision-free circular motions in challenging configurations, which exceeds the capability of standard MPC pipelines. We propose a framework that integrates force-feedback MPC with diffusion-based motion priors to address these challenges. The diffusion model serves as a memory of motion strategies, providing robust initialization and adaptation across multiple task instances, while MPC ensures safe execution with explicit force tracking, torque feasibility, and collision avoidance. We validate our approach on a torque-controlled manipulator performing industrial deburring tasks. Experiments demonstrate reliable tool insertion, accurate normal force tracking, and circular deburring motions even in hard-to-reach configurations and under obstacle constraints. To our knowledge, this is the first integration of diffusion motion priors with force-feedback MPC for collision-aware, contact-rich industrial tasks
Influence of Visual-Inertial Sensor-To-Segment Calibration on Upper Limb Joint Angles Estimation from Multiple Inverse Kinematics Methods
No full textInternational audienceThis study aims to explore the potential for accurately estimating joint angles during upper limb rehabilitation tasks with different calibration procedures, inverse kinematics methods and measurement modalities. Affordable embedded visual-inertial measurement units offer a promising alternative to the costly and cumbersome gold standard marker-based stereophotogrammetric systems. However, affordability comes with inherent sensors inaccuracies. Hence, prior to their application in a real clinical setting, it is important to demonstrate their ability for accurate joint angle estimation. Discrepancies in joint angles arise due to the inaccuracies of different sensing modalities but also to sensor-to-segment calibration procedures that significantly alter the joint offsets. Therefore, in this paper, the impact of functional and anatomical calibration procedures on joint angle estimation was compared among seven healthy young volunteers. When the same calibration procedures were applied with visual-inertial measurement units and stereophotogrammetric systems data, a relatively small root mean square error of 7.9deg and correlation coefficients exceeding 0.86 were observed. When different calibration procedures were applied with visual-inertial measurement units and stereophotogrammetric systems data, higher root mean square superior to 10deg were observed, highlighting the importance of consistency with the reference set when assessing accuracy. Furthermore, our analysis shows the benefit of using multi-body inverse kinematics procedure over treating inverse kinematics separately for each segment when dealing with inaccurate visual-inertial measurement units data
Low-Power, Logic-Only FPGA Implementation of a Deep Reinforcement Learning Locomotion Policy on a Quadruped Robot
No full textArtificial Neural Networks (ANN) and Machine Learning (ML) have become valuable tools for robotic motion, but their high computational cost typically demands complex and energy-intensive hardware. Field-Programmable Gate Arrays (FPGA) offer a promising alternative for efficient online Artificial Intelligence (AI) model inference. This work presents an FPGA-based architecture capable of handling both sensor data processing and Artificial Intelligence model inference, entirely within the Programmable Logic (PL) of the device. Applied to the SOLO12 quadruped robot, the proposed design can efficiently replace the conventional software AI algorithm implementation on Raspberry Pi and its interconnection with a dedicated Master Board. Furthermore, the FPGA implementation of the ANN has been validated in simulation by connecting the FPGA to a computer running the SOLO12 environment, as illustrated in Fig. 1. This paper focuses on the hardware implementation and optimization of ANN on a small-scale FPGA, highlighting its potential as a compact and energy-efficient solution for robotic control
Capacitive Micromachined Ultrasonic Transducer Based Airborne Pitch-Catch Measurement Modeling
No full textInternational audienc
Solving the Agile Earth Observation Satellite Scheduling Problem with CP and Local Search
No full textInternational audienceThe increasing hunger for remote sensing data fuels a boom in satellite imagery, leading to larger agile Earth observation satellite (AEOS) constellations. Therefore, instances of the AEOS scheduling problem (AEOSSP) has become harder to solve. As most existing approaches to solve AEOSSP are designed for a single spacecraft or smaller constellations in mind, they are not tailored to the need of our industrial partner that is about to launch a constellation of 20 AEOSs. Hence, we designed a local search solver able to schedule observations and downloads at such a scale. It relies on solving a series of sub-problems as travelling salesman problem with time windows (TSPTW), first greedily, then using a CP-SAT exact solver in order to find a solution when the greedy insertion fails. Lastly, it schedules downloads and enforces memory constraints with greedy algorithms. Experiments were carried out on instances from the literature as well as generated instances from a simulator we designed. Our experiments show that using CP to solve the sub-problem significantly improve the solutions, and overall our method is slightly better than state-of-the-art approaches