1 citations · 2 across the 16 of their papers we have counts for
18 papers
Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos
Constant Roux, Ludovic De Matteïs, Armand Jordana +4
Imitation Learning from monocular video demonstrations provides a scalable approach for teaching complex skills to humanoid robots. However, translating human motion to humanoids r…
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Hao Wang, Nam Nguyen, Armand Jordana +2
Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state and input constraints. Althou…
Stability-Guided Exploration for Diverse Motion Generation
Eckart Cobo-Briesewitz, Tilman Burghoff, Denis Shcherba +2
Scaling up datasets is highly effective in improving the performance of deep learning models, including in the field of robot learning. However, data collection still proves to be…
Combining Constrained Sampling and Reinforcement Learning for Manipulation
Marc Toussaint, Cornelius V. Braun, Armand Jordana +5
Training manipulation policies in contact-rich settings is a core challenge in robotics. While Reinforcement Learning (RL) has demonstrated its strength in such settings, it may st…
Consensus-based optimization (CBO): Towards Global Optimality in Robotics
Xudong Sun, Armand Jordana, Massimo Fornasier +2
Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, most existing methods (e.g., MPPI,…
Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion
Arthur Haffemayer, Alexandre Chapin, Armand Jordana +4
Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical…
Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis
Hao Wang, Armand Jordana, Ludovic Righetti +1
While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and sa…
An Introduction to Zero-Order Optimization Techniques for Robotics
Armand Jordana, Jianghan Zhang, Joseph Amigo +1
Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantag…
Infinite-Horizon Value Function Approximation for Model Predictive Control
Armand Jordana, Sébastien Kleff, Arthur Haffemayer +4
Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large…
Accelerated gradient descent for high frequency Model Predictive Control
Jianghan Zhang, Armand Jordana, Ludovic Righetti
The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While tho…
Diffusion-based learning of contact plans for agile locomotion
Victor Dhédin, Adithya Kumar Chinnakkonda Ravi, Armand Jordana +5
Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones…
Risk-Sensitive Extended Kalman Filter
Armand Jordana, Avadesh Meduri, Etienne Arlaud +2
In robotics, designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers often do not consider the estimation uncertainty and only…
MPC with Sensor-Based Online Cost Adaptation
Avadesh Meduri, Huaijiang Zhu, Armand Jordana +1
Model predictive control is a powerful tool to generate complex motions for robots. However, it often requires solving non-convex problems online to produce rich behaviors, which i…
Stagewise Newton Method for Dynamic Game Control with Imperfect State Observation
Armand Jordana, Bilal Hammoud, Justin Carpentier +1
In this letter, we study dynamic game optimal control with imperfect state observations and introduce an iterative method to find a local Nash equilibrium. The algorithm consists o…
iRiSC: Iterative Risk Sensitive Control for Nonlinear Systems with Imperfect Observations
Bilal Hammoud, Armand Jordana, Ludovic Righetti
This work addresses the problem of risk-sensitive control for nonlinear systems with imperfect state observations, extending results for the linear case. In particular, we derive a…
Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting
Armand Jordana, Justin Carpentier, Ludovic Righetti
Modeling dynamical systems plays a crucial role in capturing and understanding complex physical phenomena. When physical models are not sufficiently accurate or hardly describable…
Simultaneous Navigation and Construction Benchmarking Environments
Wenyu Han, Chen Feng, Haoran Wu +5
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major r…
The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes
Christian A. Yates, Adam George, Armand Jordana +3
The simulation of stochastic reaction-diffusion systems using fine-grained representations can become computationally prohibitive when particle numbers become large. If particle nu…