Ludovic Righetti

Max Planck Research Group Leader

Movement Generation and Control

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

If you are interested in working with us, either at MPI or NYU, please check out this page first https://wp.nyu.edu/machinesinmotion/working-with-us/ Any inquiry that does not contain: a CV, an unofiicial transcript and a clear description of your background, research interests and how they relate to our research will not receive an answer.

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

Modeling Human Movement

An expressive model of human motion is essential for action classification, motion prediction and synthesis. To that end, we are exploring several deep network architectures to predict human movement.

Current methods for motion prediction typically do not wo...

Julieta Martinez Judith Bütepage Javier Romero Hedvig Kjellström Michael Black Ludovic Righetti Partha Ghosh Sergey Prokudin

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

Physics of Body Shape and Motion

Humans live in a real world governed by the laws of physics; that is, we apply and exploit forces, such as gravity, in our daily interactions with the world. In this project we allow virtual humans to interact with a virtual world subject to the laws of physics. How would one's body shape deform in cas...

Michael Black Sergi Pujades Meekyoung Kim Gerard Pons-Moll Javier Romero Ludovic Righetti

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

Whole-body control of legged 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

Learning contact dynamics

In this project, we are interested in using machine learning techniques to handle robot behaviors with hard impact dynamics and contacts, such as walking, jumping or catching a ball. We investigate reinforcement learning techniques to learn dynamic motions under contact. We also use statistical learning tools to find representations...

Ludovic Righetti Julian Viereck Avadesh Merduri Felix Grimminger Majid Khadiv

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

Multi-contact trajectory optimization

In this project, we investigate the problem of computing movements for robots with arms and legs in intermittent contact with their environment that are physically correct. Our approach uses trajectory optimization and optimal control techniques. In particular, we study how the physics of mechanical systems impose a structure to the...

Ludovic Righetti Alexander Herzog Andrea Del Prete Maximilien Naveau Brahayam Ponton Majid Khadiv Sean Mason Nick Rotella

Sensor fusion for legged robots

In this project, we explore the problem of fusing sensor information from inertial, position and force measurements to recover quantities fundamental for the feedback control of legged robots. Our final goal is to find a systematic way of fusing multiple sensor modalities to improve the control of legged robots.

Ou...
Ludovic Righetti Nick Rotella Sean Mason Alexander Herzog Stefan Schaal

80 results (View BibTeX file of all listed publications)

2020

Walking Control Based on Step Timing Adaptation

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

IEEE Transactions on Robotics, 36, pages: 629 - 643, IEEE, June 2020 (article)

Abstract

Step adjustment can improve the gait robustness of biped robots; however, the adaptation of step timing is often neglected as it gives rise to nonconvex problems when optimized over several footsteps. In this article, we argue that it is not necessary to optimize walking over several steps to ensure gait viability and show that it is sufficient to merely select the next step timing and location. Using this insight, we propose a novel walking pattern generator that optimally selects step location and timing at every control cycle. Our approach is computationally simple compared to standard approaches in the literature, yet guarantees that any viable state will remain viable in the future. We propose a swing foot adaptation strategy and integrate the pattern generator with an inverse dynamics controller that does not explicitly control the center of mass nor the foot center of pressure. This is particularly useful for biped robots with limited control authority over their foot center of pressure, such as robots with point feet or passive ankles. Extensive simulations on a humanoid robot with passive ankles demonstrate the capabilities of the approach in various walking situations, including external pushes and foot slippage, and emphasize the importance of step timing adaptation to stabilize walking.

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