Felix Grimminger is a mechatronics engineer working in Stefan Schaal’s Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems in Tübingen, Germany. Before joining the Autonomous Motion Lab in 2013 he worked for Boston Dynamics on the BigDog project and on several different robotic projects at the German Research Center for Artificial Intelligence in Bremen, Germany. He is especially interested in mechanical design, series elastic actuation and highly dynamic legged robots.
Proceedings of the IEEE International Conference on Intelligent Robotics Systems, Chicago, IL, September 2014 (conference)
Recently several hierarchical inverse dynamicscontrollers based on cascades of quadratic programs havebeen proposed for application on torque controlled robots.They have important theoretical benefits but have never beenimplemented on a torque controlled robot where model inaccuraciesand real-time computation requirements can beproblematic. In this contribution we present an experimentalevaluation of these algorithms in the context of balance controlfor a humanoid robot. The presented experiments demonstratethe applicability of the approach under real robot conditions(i.e. model uncertainty, estimation errors, etc). We propose asimplification of the optimization problem that allows us todecrease computation time enough to implement it in a fasttorque control loop. We implement a momentum-based balancecontroller which shows robust performance in face of unknowndisturbances, even when the robot is standing on only onefoot. In a second experiment, a tracking task is evaluatedto demonstrate the performance of the controller with morecomplicated hierarchies. Our results show that hierarchicalinverse dynamics controllers can be used for feedback controlof humanoid robots and that momentum-based balance controlcan be efficiently implemented on a real robot.
In Proceedings of Dynamic Walking, Zürich, Switzerland, 2014, clmc (inproceedings)
We expect autonomous legged robots to perform complex tasks in persistent interaction with an uncertain and changing environment (e.g. in a disaster relief scenario). Therefore, we need to design algorithms that can generate precise but compliant motions while optimizing the interactions with the environment. In this context, torque control algorithms often offer high performance for motion control while guaranteeing a certain level of compliance. In addition they allow for direct control of interaction forces with the environment. Recent contributions have demonstrated the relevance of torque con- trol approaches for humanoid robots, for example for balanc- ing capabilities [5, 6]. Among those we find passivity-based approaches  that regulate the position of the Center of Mass (CoM) by applying admissible contact forces under the quasi- static assumption. On the one hand, these approaches do not rely on a precise dynamic model of the robot while natu- rally guaranteeing robustness due to the passivity property of the controller. On the other hand the quasi-static assumption might be limiting for dynamic motions. A promising way of leveraging this limitation are control algorithms that take the full dynamic model into account . However, the need for a precise dynamic model, sensor noise (particularly in the ve- locities) and limited torque bandwidth makes them more chal- lenging to implement. Moreover, it is generally required to simplify the optimization process to meet time requirements of fast control loops (typically 1 kHz on modern torque con- trolled robots). Practical evaluations of both approaches are still rare due to the lack of torque controlled humanoid plat- forms and the complexity in conducting such robot experiments.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems