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Olivier Stasse

11 accepted papers

2024

CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning

Elliot Chane-Sane, Pierre-Alexandre Leziart, Thomas Flayols, Olivier Stasse, Philippe Souères, Nicolas Mansard
IROS 2024

Deep Reinforcement Learning (RL) has demonstrated impressive results in solving complex robotic tasks such as quadruped locomotion. Yet, current solvers fail to produce efficient policies respecting hard constraints. In this work, we advocate for integrating constraints into robot learning and prese

Cited by 35Source ↗code ↗Scholar ↗
2022

Passive Inverse Dynamics Control Using a Global Energy Tank for Torque-Controlled Humanoid Robots in Multi-Contact

Noëlie Ramuzat, Sébastien Boria, Olivier Stasse
RA-L 2022

This letter presents a passivity-based inverse dynamics (ID) controller using a global energy tank. The proposed control approach allows us to achieve a safe multi-contact scenario on a torque controlled humanoid robot. The controller is primarily a task space ID quadratic programming (QP) which eff

Cited by 8Source ↗Scholar ↗
2022

Value learning from trajectory optimization and Sobolev descent: A step toward reinforcement learning with superlinear convergence properties

Amit Parag, Sébastien Kleff, Léo Saci, Nicolas Mansard, Olivier Stasse
ICRA 2022poster

The recent successes in deep reinforcement learning largely rely on the capabilities of generating masses of data, which in turn implies the use of a simulator. In particular, current progress in multi body dynamic simulators are under-pinning the implementation of reinforcement learning for end-to-…

Cited by 15Source ↗Scholar ↗
2021

Delay Aware Universal Notice Network: Real world multi-robot transfer learning

Samuel Beaussant, Sébastien Lengagne, Benoit Thuilot, Olivier Stasse
IROS 2021poster

General purpose simulators provide cheap training data to learn complex robotic skills. However, the transition from simulation to reality is often very challenging for the agent. One major issue is the delay on the physical robot that may deteriorate the performance of the deployed agent. Furthermo…

Cited by 1Source ↗Scholar ↗
2021

Human Trajectory Prediction Model and Its Coupling With a Walking Pattern Generator of a Humanoid Robot

Isabelle Maroger, Noëlie Ramuzat, Olivier Stasse, Bruno Watier
RA-L 2021

In order to smoothly perform interactions between a humanoid robot and a human, knowledge about the human locomotion can be efficiently used. Indeed, in a human-robot collaboration, a prediction model of the human behaviour allows the robot to act proactively. In this letter, an optimal control base

Cited by 24Source ↗Scholar ↗
2021

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Ewen Dantec, Rohan Budhiraja, Adria Roig, Teguh Lembono, Guilhem Saurel, Olivier Stasse +6
ICRA 2021poster

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations…

Cited by 63Source ↗Scholar ↗
2017

A Reactive Walking Pattern Generator Based on Nonlinear Model Predictive Control

Maximilien Naveau, Manuel Kudruss, Olivier Stasse, Christian Kirches, Katja D. Mombaur, Philippe Souères
RA-L 2017

The contribution of this work is to show that real-time nonlinear model predictive control (NMPC) can be implemented on position controlled humanoid robots. Following the idea of “walking without thinking,” we propose a walking pattern generator that takes into account simultaneously the position an

Cited by 106Source ↗Scholar ↗
2017

COCoMoPL: A Novel Approach for Humanoid Walking Generation Combining Optimal Control, Movement Primitives and Learning and its Transfer to the Real Robot HRP-2

Debora Clever, Monika Harant, Katja D. Mombaur, Maximilien Naveau, Olivier Stasse, Dominik M. Endres
RA-L 2017

COCoMoPL is a recently developed approach combining optimal control, movement primitives and learning for the generation of humanoid walking motions (Clever et al. Robot. Auton. Syst., vol. 83, pp. 287.298, 2016). It solves optimal control problems based on detailed dynamic models of the robot for a

Cited by 34Source ↗Scholar ↗
2016

A versatile and efficient pattern generator for generalized legged locomotion

Justin Carpentier, Steve Tonneau, Maximilien Naveau, Olivier Stasse, Nicolas Mansard
ICRA 2016

This paper presents a generic and efficient approach to generate dynamically consistent motions for under-actuated systems like humanoid or quadruped robots. The main contribution is a walking pattern generator, able to compute a stable trajectory of the center of mass of the robot along with the an

Cited by 143Source ↗Scholar ↗