Ludovic Righetti

Research Group Leader

Office: N2.010
Max-Planck-Ring 4
Tübingen
Germany

+49 7071 601 1825

https://mg.is.tuebingen.mpg.de/

Since September 2017, Ludovic Righetti is an Associate Professor in the Electrical and Computer Engineering Department and in the Mechanical and Aerospace Engineering Department at the Tandon School of Engineering of New York University. He is also a Senior Researcher at the Max-Planck Institute for Intelligent Systems (MPI-IS) in Tübingen, Germany.

He leads the Machines in Motion Laboratory at New York University and the Movement Generation and Control group at MPI-IS.

A complete list of his publications is available on his NYU laboratory website or on Google Scholar

Autonomous Robotic Manipulation

We have developed algorithms which enable an autonomous manipulation system to grasp a wide range of objects and to perform a certain number of manipulation tasks, such as drilling, using a stapler, unlocking a door with a key or changing a tire \cite{Righetti_AR_2013}. More generally, we are interested in providing complete integra...

Ludovic Righetti Mrinal Kalakrishnan Peter Pastor Manuel Wüthrich Alexander Herzog Jeannette Bohg Stefan Schaal

Inverse Optimal Control

Tuning and designing robotic behavior by combining elementary objective terms is a tedious task which generally consists of finding proper representations for each new skill. Inverse Optimal Control (IOC) allows, by specifying a set of basis functions (or features), to learn the right association of objective terms defining a policy...

Jim Mainprice Mrinal Kalakrishnan Peter Pastor Nathan Ratliff Ludovic Righetti Stefan Schaal Andreas Doerr

Model-based Control of Floating-based Robots

Legged robots are expected to locomote autonomously in an uncertain and potentially dynamically changing environment. Active interaction with contacts becomes inevitable to move and apply forces in a goal directed way and withstand unpredicted changes in the environment. Therefore, we need to design algorithms that exploit interacti...

Ludovic Righetti Alexander Herzog Nick Rotella Sean Mason Felix Grimminger Stefan Schaal

Motion Optimization

Motion generation is increasingly formalized as a large scale optimization over future outcomes of actions. For high dimensional manipulation platforms, this optimization is computationally so difficult that for a long time traditional approaches focused primarily on feasibility of the solution rather than even local optimality. Rec...

Nathan Ratliff Ludovic Righetti Jim Mainprice Jeannette Bohg Stefan Schaal

Optimal Control for Legged Robots

Planning dynamic behaviors for legged robots is a challenging task because the robot is subject to strong dynamic constraints due to its floating base (i.e. it can fall). It needs to take into account intermittent contacts with the environment and apply contact forces in order to move.

In this project, we address the probl...

Ludovic Righetti Alexander Herzog Nick Rotella Sean Mason Felix Grimminger John Rebula Brahayam Ponton Stefan Schaal

State Estimation and Sensor Fusion for the Control of Legged Robots

When developing controllers for legged robots, one assumes that important quantities like the Center of Mass of the robot (CoM), its position and orientation in space or its joint positions and velocities are know accurately to be used in feedback laws. While the estimation of such quantities is trivial in simulation, it becomes a s...

Nick Rotella Sean Mason Alexander Herzog Ludovic Righetti Stefan Schaal

Template-Based Learning of Model Free Grasping

Autonomous robotic grasping is one of the pre-requisites for personal robots to become useful when assisting humans in households. Seamlessly easy for humans, it still remains a very challenging task for robots. The key problem of robotic grasping is to automatically choose an appropriate grasp configuration given an object as perce...

Alexander Herzog Peter Pastor Mrinal Kalakrishnan Ludovic Righetti Jeannette Bohg Stefan Schaal

Movement Representation for Reactive Behavior

An important part of our research is concerned with the problem of movement representations. The way motion and contacts are represented is crucial to derive efficient planning and control algorithms, for example by significantly simplifying the underlying optimization problems. The way sensory information can be integrated to gen...

Ludovic Righetti Stefan Schaal Peter Pastor Mrinal Kalakrishnan

80 results (View BibTeX file of all listed publications)

2018

Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract

Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

pdf video [BibTex]

2018

ps Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J. Robust Physics-based Motion Retargeting with Realistic Body Shapes Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

pdf video [BibTex]

On Time Optimization of Centroidal Momentum Dynamics

Ponton, B., Herzog, A., Prete, A. D., Schaal, S., Righetti, L.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

Abstract

Recently, the centroidal momentum dynamics has received substantial attention to plan dynamically consistent motions for robots with arms and legs in multi-contact scenarios. However, it is also non convex which renders any optimization approach difficult and timing is usually kept fixed in most trajectory optimization techniques to not introduce additional non convexities to the problem. But this can limit the versatility of the algorithms. In our previous work, we proposed a convex relaxation of the problem that allowed to efficiently compute momentum trajectories and contact forces. However, our approach could not minimize a desired angular momentum objective which seriously limited its applicability. Noticing that the non-convexity introduced by the time variables is of similar nature as the centroidal dynamics one, we propose two convex relaxations to the problem based on trust regions and soft constraints. The resulting approaches can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable. The performance of the algorithm is demonstrated in several multi-contact scenarios for a humanoid robot. In particular, we show that the proposed convex relaxation of the original problem finds solutions that are consistent with the original non-convex problem and illustrate how timing optimization allows to find motion plans that would be difficult to plan with fixed timing.

video paper [BibTex]

am Ponton, B., Herzog, A., Prete, A. D., Schaal, S., Righetti, L. On Time Optimization of Centroidal Momentum Dynamics In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

video paper [BibTex]

2016

The Role of Measurement Uncertainty in Optimal Control for Contact Interactions

Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

Abstract

Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications that involve interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of pre- cise knowledge of the world, which is not an actual disturbance. We de- velop a computationally efficient SOC algorithm, based on risk-sensitive control, that takes into account uncertainty in the measurements. We include the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. We show that high measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise variance that creates stiff behaviors. Simulation results on a simple 2D manipulator show that our controller can create better interaction with the environment under uncertain contact locations than traditional SOC approaches.

arXiv [BibTex]

2016

am The Role of Measurement Uncertainty in Optimal Control for Contact Interactions Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

arXiv [BibTex]

Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

Herzog, A., Rotella, N., Mason, S., Grimminger, F., Schaal, S., Righetti, L.

Autonomous Robots, 40(3):473-491, March 2016 (article)

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video pdf DOI Project Page Project Page [BibTex]

am mg Herzog, A., Rotella, N., Mason, S., Grimminger, F., Schaal, S., Righetti, L. Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid Autonomous Robots, 40(3):473-491, March 2016 (article)

video pdf DOI Project Page Project Page [BibTex]

A Convex Model of Humanoid Momentum Dynamics for Multi-Contact Motion Generation

Ponton, B., Herzog, A., Schaal, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

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arxiv video [BibTex]

am mg Ponton, B., Herzog, A., Schaal, S., Righetti, L. A Convex Model of Humanoid Momentum Dynamics for Multi-Contact Motion Generation Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

arxiv video [BibTex]

Stepping stabilization using a combination of DCM tracking and step adjustment

Khadiv, M., Kleff, S., Herzog, A., Moosavian, S., Schaal, S., Righetti, L.

4th RSI International Conference on Robotics and Mechatronics, 2016 (conference)

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arxiv [BibTex]

am mg Khadiv, M., Kleff, S., Herzog, A., Moosavian, S., Schaal, S., Righetti, L. Stepping stabilization using a combination of DCM tracking and step adjustment 4th RSI International Conference on Robotics and Mechatronics, 2016 (conference)

arxiv [BibTex]

Structured contact force optimization for kino-dynamic motion generation

Herzog, A., Schaal, S., Righetti, L.

In International Conference on Intelligent Robots and Systems (IROS) 2016, pages: 2703-2710, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 (inproceedings)

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video pdf link (url) [BibTex]

am mg Herzog, A., Schaal, S., Righetti, L. Structured contact force optimization for kino-dynamic motion generation In International Conference on Intelligent Robots and Systems (IROS) 2016, pages: 2703-2710, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 (inproceedings)

video pdf link (url) [BibTex]

Step Timing Adjustment: A Step Toward Generating Robust Gaits

Khadiv, M., Herzog, A., Moosavian, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

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arxiv video [BibTex]

am mg Khadiv, M., Herzog, A., Moosavian, S., Righetti, L. Step Timing Adjustment: A Step Toward Generating Robust Gaits Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

arxiv video [BibTex]

Balancing and Walking Using Full Dynamics LQR Control with Contact Constraints

Mason, S., rotella, N., Schaal, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

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