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Avadesh Meduri

10 accepted papers

2026

Structure-Exploiting Sequential Quadratic Programming for Model-Predictive Control

Armand Jordana, Sebastien Kleff, Avadesh Meduri, Justin Carpentier, Nicolas Mansard, Ludovic Righetti
ICRA 2026poster

The promise of model-predictive control (MPC) in robotics has led to extensive development of efficient numerical optimal control solvers in line with differential dynamic programming because it exploits the sparsity induced by time. In this work, we argue that this effervescence has hidden the fact…

Cited by 0Source ↗Scholar ↗
2025

Cost Function Estimation Using Inverse Reinforcement Learning with Minimal Observations

Sarmad Mehrdad, Avadesh Meduri, Ludovic Righetti
IROS 2025

We present an iterative inverse reinforcement learning algorithm to infer optimal cost functions in continuous spaces. Based on a popular maximum entropy criteria, our approach iteratively finds a weight improvement step and proposes a method to find an appropriate step size that ensures learned cos

Cited by 4Source ↗Scholar ↗
2024

Risk-Sensitive Extended Kalman Filter

Armand Jordana, Avadesh Meduri, Etienne Arlaud, Justin Carpentier, Ludovic Righetti
ICRA 2024poster

Designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers seldom consider estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes in the environment or the robot’s dynamics can lead to catastrophic behav…

Cited by 3Source ↗code ↗Scholar ↗
2023

Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion

Victor Dhédin, Haolong Li, Shahram Khorshidi, Lukas Mack, Adithya Kumar Chinnakkonda Ravi, Avadesh Meduri +5
ICRA 2023poster

Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a…

Cited by 11Source ↗Scholar ↗
2021

DeepQ Stepper: A framework for reactive dynamic walking on uneven terrain

Avadesh Meduri, Majid Khadiv, Ludovic Righetti
ICRA 2021poster

Reactive stepping and push recovery for biped robots is often restricted to flat terrains because of the difficulty in computing capture regions for nonlinear dynamic models. In this paper, we address this limitation by proposing a novel 3D reactive stepper, the DeepQ stepper, that can approximately…

Cited by 14Source ↗Scholar ↗
2021

High-Frequency Nonlinear Model Predictive Control of a Manipulator

Sebastien Kleff, Avadesh Meduri, Rohan Budhiraja, Nicolas Mansard, Ludovic Righetti
ICRA 2021poster

Model Predictive Control (MPC) promises to endow robots with enough reactivity to perform complex tasks in dynamic environments by frequently updating their motion plan based on measurements. Despite its appeal, it has seldom been deployed on real machines because of scaling constraints. This paper…

Cited by 93Source ↗Scholar ↗
2021

Rapid Convex Optimization of Centroidal Dynamics using Block Coordinate Descent

Paarth Shah, Avadesh Meduri, Wolfgang Merkt, Majid Khadiv, Ioannis Havoutis, Ludovic Righetti
IROS 2021poster

In this paper we explore the use of block coordinate descent (BCD) to optimize the centroidal momentum dynamics for dynamically consistent multi-contact behaviors. The centroidal dynamics have recently received a large amount of attention in order to create physically realizable motions for robots w…

Cited by 12Source ↗Scholar ↗
2020

An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research

Felix Grimminger, Avadesh Meduri, Majid Khadiv, Julian Viereck, Manuel Wüthrich, Maximilien Naveau +7
RA-L 2020

We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor and a low-gear-ratio transmission suitable for impedance and force control. We also present a novel foot contact sensor

Cited by 238Source ↗code ↗Scholar ↗