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Control of contact interactions for robots acting in the world

Total Cost €


EC-Contrib. €






Project "CONT-ACT" data sheet

The following table provides information about the project.


Organization address
postcode: 80539
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙495˙500 €
 EC max contribution 1˙495˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2014-STG
 Funding Scheme ERC-STG
 Starting year 2015
 Duration (year-month-day) from 2015-06-01   to  2020-05-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

What are the algorithmic principles that would allow a robot to run through a rocky terrain, lift a couch while reaching for an object that rolled under it or manipulate a screwdriver while balancing on top of a ladder? Answering this seemingly naïve question resorts to understanding the fundamental principles for robot locomotion and manipulation, which is very challenging. However, it is a necessary step towards ubiquitous robots capable of helping humans in an uncountable number of tasks. The fundamental aspect of both locomotion and manipulation is that the dynamic interaction of the robot with its environment through the creation of physical contacts is at the heart of the tasks. The planning of such interactions in a general manner is an unsolved problem. Moreover, it is not clear how sensory information (e.g. tactile and force sensors) can be included to improve the robustness of robot behaviors. Most of the time, it is simply discarded. CONT-ACT has the ambition to develop a consistent theoretical framework for motion generation and control where contact interaction is at the core of the approach and an efficient use of sensory information drives the development of high performance, adaptive and robust planning and control methods. CONT-ACT develops an architecture based on real-time predictive controllers that fully exploit contact interactions. In addition, the structure of sensory information during contact interactions is experimentally analyzed to create sensor representations adapted for control. It is then possible to learn predictive models in sensor space that are used to create very reactive controllers. The robot constantly improves its performance as it learns better sensory models. It is a step towards a general theory for robot movement that can be used to control any robot with legs and arms for both manipulation and locomotion tasks and that allows robots to constantly improve their performances as they experience the world.


year authors and title journal last update
List of publications.
2019 Yeganegi, Mohammad Hasan; Khadiv, Majid; Moosavian, S. Ali A.; Zhu, Jia-Jie; Del Prete, Andrea; Righetti, Ludovic
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning
published pages: , ISSN: , DOI:
IEEE-RAS International Conference on Humanoid Robots 2020-02-06
2019 Sarah Bechtle, Yixin Lin, Akshara Rai, Ludovic Righetti, Franziska Meier
Curious iLQR: Resolving Uncertainty in Model-based RL
published pages: , ISSN: , DOI:
Conference on Robot Learning (CoRL) 2020-02-06
2019 John R. Rebula, Stefan Schaal, James Finley, Ludovic Righetti
A Robustness Analysis of Inverse Optimal Control of Bipedal Walking
published pages: 4531-4538, ISSN: 2377-3766, DOI: 10.1109/lra.2019.2933766
IEEE Robotics and Automation Letters 4/4 2020-02-06
2018 Nicholas Rotella, Stefan Schaal, Ludovic Righetti
Unsupervised Contact Learning for Humanoid Estimation and Control
published pages: , ISSN: , DOI:
IEEE-RAS International Conference on Robotics and Automation 2019-05-29
2018 Julian Viereck, Jules Kozolinsky, Alexander Herzog, Ludovic Righetti
Learning a Structured Neural Network Policy for a Hopping Task
published pages: , ISSN: 2377-3766, DOI:
IEEE Robotics and Automation Letters 4 2019-05-29
2018 Brahayam Ponton, Alexander Herzog, Stefan Schaal, Ludovic Righetti
On Time Optimisation of Centroidal Momentum Dynamics
published pages: , ISSN: , DOI:
IEEE-RAS International Conference on Robotics and Automation 2019-05-29
2016 B. Ponton, S. Schaal and L. Righetti
On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions
published pages: , ISSN: , DOI:
12th International Workshop on the Algorithmic Foundations of Robotics 2019-05-29
2017 Majid Khadiv, S. Ali A. Moosavian, Alexander Herzog, Ludovic Righetti
Pattern Generation for Walking on Slippery Terrains
published pages: , ISSN: , DOI:
5th RSI International Conference on Robotics and Mechatronics (ICROM) 2019-05-29
2018 Sean Mason, Nicholas Rotella, Stefan Schaal, Ludovic Righetti
A MPC Walking Framework With External Contact Forces
published pages: , ISSN: , DOI:
IEEE-RAS International Conference on Robotics and Automation 2019-05-29
2016 M Khadiv, S Kleff, A Herzog, SAA Moosavian, S Schaal and L Righetti
Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment
published pages: , ISSN: , DOI:
4th RSI International Conference on Robotics and Mechatronics 2019-05-29

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