no code implementations • 20 Sep 2024 • Elliot Chane-Sane, Joseph Amigo, Thomas Flayols, Ludovic Righetti, Nicolas Mansard
This allows the robot to adapt behaviors from this privileged experience to visual locomotion while circumventing the high computational costs of RL directly from pixels.
no code implementations • 5 Apr 2022 • Sarah Bechtle, Ludovic Righetti, Franziska Meier
In this paper we present a framework to meta-learn the critic for gradient-based policy learning.
no code implementations • 14 Jul 2021 • Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti
We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any additional training using a single demonstration generated by trajectory optimization.
1 code implementation • 22 Jun 2021 • Armand Jordana, Justin Carpentier, Ludovic Righetti
In this work, we introduce a generic and scalable method based on multiple shooting to learn latent representations of indirectly observed dynamical systems.
1 code implementation • 31 Mar 2021 • Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dong Liu, Lerrel Pinto, Ludovic Righetti
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design.
no code implementations • 1 Jan 2021 • Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dongdong Liu, Lerrel Pinto, Ludovic Righetti
We need intelligent robots to perform mobile construction, the process of moving in an environment and modifying its geometry according to a design plan.
1 code implementation • 16 Oct 2020 • Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe
We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation.
no code implementations • 9 Oct 2020 • Mohammad Hasan Yeganegi, Majid Khadiv, Andrea Del Prete, S. Ali A. Moosavian, Ludovic Righetti
In this approach, instead of adding a (conservative) terminal constraint to the problem, we propose to use the measured state projected to the viability kernel in the OCP solved at each control cycle.
Robotics
2 code implementations • 8 Aug 2020 • Manuel Wüthrich, Felix Widmaier, Felix Grimminger, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Bernhard Schölkopf, Stefan Bauer
Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.
1 code implementation • 30 Sep 2019 • Felix Grimminger, Avadesh Meduri, Majid Khadiv, Julian Viereck, Manuel Wüthrich, Maximilien Naveau, Vincent Berenz, Steve Heim, Felix Widmaier, Thomas Flayols, Jonathan Fiene, Alexander Badri-Spröwitz, Ludovic Righetti
Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation.
Robotics
no code implementations • 25 Sep 2019 • Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
2 code implementations • 11 Sep 2019 • Carlos Mastalli, Rohan Budhiraja, Wolfgang Merkt, Guilhem Saurel, Bilal Hammoud, Maximilien Naveau, Justin Carpentier, Ludovic Righetti, Sethu Vijayakumar, Nicolas Mansard
Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP).
Robotics Optimization and Control
no code implementations • 10 Aug 2019 • Miroslav Bogdanovic, Ludovic Righetti
In this paper we present a novel approach for efficient exploration that leverages previously learned tasks.
no code implementations • 17 Jul 2019 • Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti
We propose learning a policy giving as output impedance and desired position in joint space and compare the performance of that approach to torque and position control under different contact uncertainties.
1 code implementation • 12 Jun 2019 • Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
no code implementations • 15 Apr 2019 • Sarah Bechtle, Yixin Lin, Akshara Rai, Ludovic Righetti, Franziska Meier
In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty.
1 code implementation • 19 Sep 2018 • Hamza Merzic, Miroslav Bogdanovic, Daniel Kappler, Ludovic Righetti, Jeannette Bohg
While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.
no code implementations • 28 Jul 2016 • Brahayam Ponton, Alexander Herzog, Stefan Schaal, Ludovic Righetti
This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized.
Robotics
no code implementations • 29 Apr 2015 • Manuel Wüthrich, Peter Pastor, Ludovic Righetti, Aude Billard, Stefan Schaal
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking.