ERC starting grant CONT-ACT

Control of contact interactions for robots acting in the world (June 2015 - November 2020)



Objectives

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? By trying to answer these questions in CONT-ACT, we would like to understand the fundamental principles for robot locomotion and manipulation and endow robots with the robustness and adaptability necessary to efficiently and autonomously act in an unknown and changing environment. It is a necessary step towards a new technological age: ubiquitous robots capable of helping humans in an uncountable number of tasks.

Dynamic interactions of the robot with its environment through the creation of intermittent physical contacts is central to any locomotion or manipulation task. Indeed, in order to walk or manipulate an object, a robot needs to constantly physically interact with the environment and surrounding objects. Our approach to motion generation and control in CONT-ACT gives a central place to contact interactions. Our main hypothesis is that it will allow us to develop more adaptive and robust planning and control algorithms for locomotion and manipulation. The project is divided in three main objectives: 1) the development of a hierarchical receding horizon control architecture for multi-contact behaviors, 2) the development of algorithms to learn representations for motion generation through multi-modal sensing (e.g. force and touch sensing) and 3) the development of controllers based on multi-modal sensory information through optimal control and reinforcement learning.

Publications

2021

  1. J. Viereck, L. Righetti, "Learning a Centroidal Motion Planner for Legged Locomotion," in 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA), May, 2021.
  2. S. Bechtle, B. Hammoud, A. Rai, F. Meier, L. Righetti, "Leveraging Forward Model Prediction Error for Learning Control," in 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA), May, 2021.
  3. E. Daneshmand, M. Khadiv, F. Grimminger, L. Righetti, "Variable Horizon MPC with Swing Foot Dynamics for Bipedal Walking Control," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2349–2356, Apr, 2021.
  4. T. Flayols, A. Del Prete, M. Khadiv, N. Mansard, L. Righetti, "Reactive Balance Control for Legged Robots under Visco-Elastic Contacts," Applied Sciences, vol. 11, no. 11, pp. 353, 2021.
  5. B. Hammoud, M. Khadiv, L. Righetti, "Impedance Optimization for Uncertain Contact Interactions Through Risk Sensitive Optimal Control," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4766–4773, Jul, 2021.
  6. S. Kleff, A. Meduri, R. Budhiraja, N. Mansard, L. Righetti, "High-Frequency Nonlinear Model Predictive Control of a Manipulator," in 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA), May, 2021.
  7. A. Meduri, M. Khadiv, L. Righetti, "DeepQ Stepper: A Framework for Reactive Dynamic Walking on Uneven Terrain," in 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA), May, 2021.
  8. B. Ponton, M. Khadiv, A. Meduri, L. Righetti, "Efficient Multi-Contact Pattern Generation with Sequential Convex Approximations of the Centroidal Dynamics," IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1661–1679, Feb, 2021.
  9. S. Bechtle, A. Molchanov, Y. Chebotar, E. Grefenstette, L. Righetti, G. S. Sukhatme, F. Meier, "Meta-learning via learned loss," in 25th International Conference on Pattern Recognition, Jan, 2021.

2020

  1. M. Bogdanovic, M. Khadiv, L. Righetti, "Learning Variable Impedance Control for Contact Sensitive Tasks," IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6129–6136, 2020.
  2. F. Grimminger, A. Meduri, M. Khadiv, J. Viereck, M. Wüthrich, M. Naveau, V. Berenz, S. Heim, F. Widmaier, T. Flayols, J. Fiene, A. Badri-Spröwitz, L. Righetti, "An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3655–3662, Apr, 2020.
  3. M. Khadiv, A. Herzog, S. A. A. Moosavian, L. Righetti, "Walking Control Based on Step Timing Adaptation," IEEE Transactions on Robotics, vol. 36, no. 3, pp. 629–643, Apr, 2020.
  4. C. Mastalli, R. Budhiraja, W. Merkt, G. Saurel, B. Hammoud, M. Naveau, J. Carpentier, L. Righetti, S. Vijayakumar, N. Mansard, "Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control," in 2020 IEEE-RAS International Conference on Robotics and Automation (ICRA), pp. 2536–2542, May, 2020.

2019

  1. S. Bechtle, Y. Lin, A. Rai, L. Righetti, F. Meier, "Curious iLQR: Resolving Uncertainty in Model-based RL," in Proceedings of the Conference on Robot Learning, pp. 162–171, Nov, 2019.
  2. M. Bogdanovic, L. Righetti, "Learning to Explore in Motion and Interaction Tasks," in IEEE/RSJ International Conference on Intelligent Robots and Systems, Nov, 2019.
  3. Y. Lin, B. Ponton, L. Righetti, D. Berenson, "Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction," in 2019 IEEE International Conference on Robotics and Automation (ICRA), pp. 5280–5286, May, 2019.
  4. H. Merzic, M. Bogdanovic, D. Kappler, L. Righetti, J. Bohg, "Leveraging Contact Forces for Learning to Grasp," in 2019 IEEE International Conference on Robotics and Automation (ICRA), pp. 3615–3621, May, 2019.
  5. J. Rebula, S. Schaal, J. Finley, L. Righetti, "A Robustness Analysis of Inverse Optimal Control of Bipedal Walking," IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4531–4538, Aug, 2019.
  6. M. Yeganegi, M. Khadiv, S. A. A. Moosavian, J. Zhu, A. Del Prete, L. Righetti, "Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning," in IEEE-RAS International Conference on Humanoid Robots, pp. 186–193, Oct, 2019.

2018

  1. A. Gams, S. Mason, A. Ude, S. Schaal, L. Righetti, "Learning Task-Specific Dynamics to Improve Whole-Body Control," in 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Nov, 2018.
  2. S. Mason, N. Rotella, S. Schaal, L. Righetti, "An MPC Walking Framework With External Contact Forces," in 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1785–1790, May, 2018.
  3. B. Ponton, A. Herzog, A. Del Prete, S. Schaal, L. Righetti, "On Time Optimization of Centroidal Momentum Dynamics," in 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 5776–5782, May, 2018.
  4. N. Rotella, S. Schaal, L. Righetti, "Unsupervised Contact Learning for Humanoid Estimation and Control," in 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 411–417, May, 2018.
  5. J. Viereck, J. Kozolinsky, A. Herzog, L. Righetti, "Learning a Structured Neural Network Policy for a Hopping Task.," IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4092–4099, Oct, 2018.

2017

  1. M. Khadiv, S. A. A. Moosavian, A. Herzog, L. Righetti, "Pattern Generation for Walking on Slippery Terrains," in 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM), Aug, 2017.
  2. L. Righetti, A. Herzog, "Momentum-Centered Control of Contact Interactions," pp. 339–359, 2017.

2016

  1. A. Herzog, S. Schaal, L. Righetti, "Structured contact force optimization for kino-dynamic motion generation," in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2703–2710, Oct, 2016.
  2. M. Khadiv, A. Herzog, S. A. A. Moosavian, L. Righetti, "Step Timing Adjustement: a Step toward Generating Robust Gaits," in 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 35–42, Nov, 2016.
  3. M. Khadiv, S. Kleff, A. Herzog, S. A. A. Moosavian, S. Schaal, L. Righetti, "Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment," in 2016 4th International Conference on Robotics and Mechatronics (ICROM), pp. 130–135, Oct, 2016.
  4. S. Mason, N. Rotella, S. Schaal, L. Righetti, "Balancing and Walking Using Full Dynamics LQR Control With Contact Constraints," in 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 63–68, Nov, 2016.
  5. B. Ponton, S. Schaal, L. Righetti, "On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions," in Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop on the Algorithmic Foundations of Robotics, pp. 784–799, 2016.
  6. B. Ponton, A. Herzog, S. Schaal, L. Righetti, "A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation," in 2016 IEEE-RAS 16th International Conference on Humanoid Robots Humanoids, pp. 842–849, Nov, 2016.
  7. N. Rotella, S. Mason, S. Schaal, L. Righetti, "Inertial Sensor-Based Humanoid Joint State Estimation," in 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1825–1831, May, 2016.

2015

  1. A. Herzog, N. Rotella, S. Schaal, L. Righetti, "Trajectory generation for multi-contact momentum control," in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 874–880, 2015.
  2. N. Rotella, A. Herzog, S. Schaal, L. Righetti, "Humanoid Momentum Estimation Using Sensed Contact Wrenches," in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 556–563, 2015.