Header logo is

Learning Task-Specific Dynamics to Improve Whole-Body Control

2018

Conference Paper

am

mg


In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, high feedback terms can be used to improve tracking accuracy; however, this can lead to very stiff behavior or poor tracking accuracy due to limited control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. With a systematic approach we also reduce heuristic tuning of the model parameters and feedback gains, often present in real-world experiments. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach.

Author(s): Gams, Andrej and Mason, Sean and Ude, Ales and Schaal, Stefan and Righetti, Ludovic
Book Title: Hua
Year: 2018
Month: November
Publisher: IEEE

Department(s): Autonomous Motion, Movement Generation and Control
Bibtex Type: Conference Paper (inproceedings)

Address: Beijing, China
URL: https://arxiv.org/abs/1803.01978

BibTex

@inproceedings{gams_learning_2018,
  title = {Learning {Task}-{Specific} {Dynamics} to {Improve} {Whole}-{Body} {Control}},
  author = {Gams, Andrej and Mason, Sean and Ude, Ales and Schaal, Stefan and Righetti, Ludovic},
  booktitle = {Hua},
  publisher = {IEEE},
  address = {Beijing, China},
  month = nov,
  year = {2018},
  url = {https://arxiv.org/abs/1803.01978},
  month_numeric = {11}
}