Ludovic De Matteis

Ludovic De Matteis

PhD Student in robotics - Science enthousiast and keen learner

Posts by Collection

portfolio

publications

TD-CD-MPPI: Temporal-Difference Constraint-Discounted Model Predictive Path Integral Control

Pietro Noah Crestaz, Ludovic De Matteis, Elliot Chane-Sane, Nicolas Mansard and Andrea Del Prete

Published in IEEE Robotics and Automation Letters (RA-L), 2026

In this paper, we present a extension of the Model Predictive Path Integral (MPPI) method to first accounts for constraints in the optimization problem through a discount modulation strategy and second allow longer horizon while avoiding the curse of dimensionality by temporal-difference learning. The method is applied to locomotion on a quadrupedal robot. Read more

CLEO: Closed-Loop kinematics Evolutionary Optimization of bipedal structures

Virgile Batto, Ludovic De Matteis, Thomas Flayols, Margot Vulliez and Nicolas Mansard

In this paper, we propose a general approach to assist the design of serial-parallel humanoid legs using an evolutionary optimization strategy. The optimization problem incorporates design constraints and locomotion-task requirements as objective functions. It uses parallelized trajectory evaluation for efficient exploration of the design space. Read more

Collision Avoidance in Model Predictive Control using Velocity Damper

Arthur Haffemayer, Armand Jordana, Ludovic De Matteis, Krzysztof Wojciechowski, Ludovic Righetti, Florent Lamiraux, Nicolas Mansard

Published in 2025 IEEE International Conference on Robotics and Automation (ICRA)

In this paper, we propose a method to include collision avoidance in the control of a manipulator robot based on a velocity damper model. This allows to account for approach velocity and permits collision avoidance for a wider spectrum of applications than classical avoidance methods. We implemented our method on a Panda robot to perform several comparisons. In this paper, I mainly contributed to writing the derivatives of the collision avoidance constraint with respect to the robot state and controls in order to use it in a DDP setting. Read more

Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot

Constant Roux, Elliot Chane-Sane, Ludovic De Matteis, Thomas Flayols, Jérôme Manhes, Olivier Stasse, Philippe Souères

Published in 2025 IEEE International Conference on Humanoid Robots (ICHR)

In this paper, we present a methodology that leverages Constraints-as-Terminations (CaT) and domain randomization techniques to enable sim-to-real transfer. Through a series of qualitative and quantitative experiments, we evaluate our approach in terms of balance maintenance, velocity control, and responses to slip and push disturbances. Read more

Extended URDF: Accounting for parallel mechanism in robot description

Ludovic De Matteis, Virgile Batto and Nicolas Mansard

Published in 2025 International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

In this paper, we introduce an extension to the widely used Unified Robot Description Format (URDF) to support closed-loop kinematic structures. Our approach relies on augmenting URDF with minimal additional information to allow more efficient modeling of complex robotic systems while maintaining compatibility with existing design and simulation frameworks. Read more

Optimal Control of Closed Loop Walkers

Ludovic De Matteis, Virgile Batto, Justin Carpentier and Nicolas Mansard

Published in 2025 IEEE International Conference on Intelligent Robots and Systems (IROS)

In this paper, we present a method for controlling robots with closed kinematic loops (such as parallel actuation). We propose to use a serial model - for instance based on an underlying main chain - and to use additionnal constraints to account for closures. We demonstrate the advatages of this method by comparing it to a classical simplified model of the robot on several motions (walk, jump, sidewalk, and climbing stairs). Read more

Control of humanoid robots with parallel mechanisms using kinematic actuation models

Victor Lutz, Ludovic De Matteis, Virgile Batto and Nicolas Mansard

Published in 2026 IEEE/RJS International Conference on Robotics and Automation (ICRA)

In this paper, we present a method for controlling robots with closed kinematic loops (such as parallel actuation). We propose demonstrate a method to take efficiently into account the closed kinematics by using analytical formulations of different mechanisms. Read more

softwares

Example Parallel Robots

This repos contains a collection of models of robots with parallel actuation. We provide loaders to get these models in a RigidBody simulation software such as Pinocchio. Read more

Toolbox Parallel Robots

This repos contains some usefull tool to work with robots containing closed kinematic loops. It allows forward and inverse kinematics, loop closure tools, automatics joints adaptations… Read more

talks

teaching