Towards robust online inverse dynamics learning

@article{Meier2016TowardsRO,
  title={Towards robust online inverse dynamics learning},
  author={Franziska Meier and Daniel Kappler and Nathan D. Ratliff and Stefan Schaal},
  journal={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2016},
  pages={4034-4039}
}
Learning of inverse dynamics modeling errors is key for compliant or force control when analytical models are only rough approximations. Thus, designing real time capable function approximation algorithms has been a necessary focus towards the goal of online model learning. However, because these approaches learn a mapping from actual state and acceleration to torque, good tracking is required to observe data points on the desired path. Recently it has been shown how online gradient descent on… CONTINUE READING