Learning Inverse Dynamics Models with Contacts

Roberto Calandra (1) , Serena Ivaldi (1, 2) , Marc Peter Deisenroth (3) , Elmar Rueckert (1) , Jan Peters (4, 1)

1 IAS - Intelligent Autonomous Systems
2 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
3 Imperial College London
4 Max Planck Institute for Intelligent Systems [Tübingen]

— In whole-body control, joint torques and external forces need to be estimated accurately. In principle, this can be done through pervasive joint-torque sensing and accurate system identification. However, these sensors are expensive and may not be integrated in all links. Moreover, the exact position of the contact must be known for a precise estimation. If contacts occur on the whole body, tactile sensors can estimate the contact location, but this requires a kinematic spatial calibration, which is prone to errors. Accumulating errors may have dramatic effects on the system identification. As an alternative to classical model-based approaches we propose a data-driven mixture-of-experts learning approach using Gaus-sian processes. This model predicts joint torques directly from raw data of tactile and force/torque sensors. We compare our approach to an analytic model-based approach on real world data recorded from the humanoid iCub. We show that the learned model accurately predicts the joint torques resulting from contact forces, is robust to changes in the environment and outperforms existing dynamic models that use of force/ torque sensor data.

Domaines: Automatique / Robotique

Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Elmar Rueckert, Jan Peters. Learning Inverse Dynamics Models with Contacts. Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), May 2015, Seattle, WA, United States. ⟨hal-01131611⟩