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Sébastien Kleff

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4 papers

1 author row

ICRA Conference 2024 Conference Paper

Force Feedback Model-Predictive Control via Online Estimation

Armand Jordana
Sébastien Kleff
Justin Carpentier
Nicolas Mansard
Ludovic Righetti

Nonlinear model-predictive control has recently shown its practicability in robotics. However it remains limited in contact interaction tasks due to its inability to leverage sensed efforts. In this work, we propose a novel model-predictive control approach that incorporates direct feedback from force sensors while circumventing explicit modeling of the contact force evolution. Our approach is based on the online estimation of the discrepancy between the force predicted by the dynamics model and force measurements, combined with high-frequency nonlinear model-predictive control. We report an experimental validation on a torque-controlled manipulator in challenging tasks for which accurate force tracking is necessary. We show that a simple reformulation of the optimal control problem combined with standard estimation tools enables to achieve state-of-the-art performance in force control while preserving the benefits of model-predictive control, thereby outperforming traditional force control techniques. This work paves the way toward a more systematic integration of force sensors in model predictive control.

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IROS Conference 2022 Conference Paper

Introducing Force Feedback in Model Predictive Control

Sébastien Kleff
Ewen Dantec
Guilhem Saurel
Nicolas Mansard
Ludovic Righetti

In the literature about model predictive control (MPC), contact forces are planned rather than controlled. In this paper, we propose a novel paradigm to incorporate effort measurements into a predictive controller, hence allowing to control them by direct measurement feedback. We first demonstrate why the classical optimal control formulation, based on position and velocity state feedback, cannot handle direct feedback on force information. Following previous approaches in force control, we then propose to augment the classical formulations with a model of the robot actuation, which naturally allows to generate online trajectories that adapt to sensed position, velocity and torques. We propose a complete implementation of this idea on the upper part of a real humanoid robot, and show through hardware experiments that this new formulation incorporating effort feedback outperforms classical MPC in challenging tasks where physical interaction with the environment is crucial.

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ICRA Conference 2022 Conference Paper

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

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-end control of robotic systems. Yet simulators are mostly considered as black boxes while we have the knowledge to make them produce a richer information. In this paper, we are proposing to use the derivatives of the simulator to help with the convergence of the learning. For that, we combine model-based trajectory optimization to produce informative trials using 1st- and 2nd-order simulation derivatives. These locally-optimal runs give fair estimates of the value function and its derivatives, that we use to accelerate the convergence of the critics using Sobolev learning. We empirically demonstrate that the algorithm leads to a faster and more accurate estimation of the value function. The resulting value estimate is used in model-predictive controller as a proxy for shortening the preview horizon. We believe that it is also a first step toward superlinear reinforcement learning algorithm using simulation derivatives, that we need for end-to-end legged locomotion.

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ICRA Conference 2021 Conference Paper

High-Frequency Nonlinear Model Predictive Control of a Manipulator

Sébastien Kleff
Avadesh Meduri
Rohan Budhiraja
Nicolas Mansard
Ludovic Righetti

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 presents the first hardware implementation of closed-loop nonlinear MPC on a 7-DoF torque-controlled robot. Our controller leverages a state-of-the art optimal control solver, namely Differential Dynamic Programming (DDP), in order to replan state and control trajectories at real-time rates (1kHz). In addition to this experimental proof of concept, an exhaustive performance analysis shows that our controller outperforms open-loop MPC on a rapid cyclic end-effector task. We also exhibit the importance of a sufficient preview horizon and full robot dynamics through comparisons with inverse dynamics and kinematic optimization.

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