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Computed Torque Control with Nonparametric Regression Models

2008

Conference Paper

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Computed torque control allows the design of considerably more precise, energy-efficient and compliant controls for robots. However, the major obstacle is the requirement of an accurate model for torque generation, which cannot be obtained in some cases using rigid-body formulations due to unmodeled nonlinearities, such as complex friction or actuator dynamics. In such cases, models approximated from robot data present an appealing alternative. In this paper, we compare two nonparametric regression methods for model approximation, i.e., locally weighted projection regression (LWPR) and Gaussian process regression (GPR). While locally weighted regression was employed for real-time model estimation in learning adaptive control, Gaussian process regression has not been used in control to-date due to high computational requirements. The comparison includes the assessment of model approximation for both regression methods using data originated from SARCOS robot arm, as well as an evaluation of the robot tracking p erformance in computed torque control employing the approximated models. Our results show that GPR can be applied for real-time control achieving higher accuracy. However, for the online learning LWPR is superior by reason of lower computational requirements.

Author(s): Nguyen-Tuong, D. and Seeger, M. and Peters, J.
Book Title: ACC 2008
Journal: Proceedings of the 2008 American Control Conference (ACC 2008)
Pages: 212-217
Year: 2008
Month: June
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Piscataway, NJ, USA
Digital: 0
DOI: 10.1109/ACC.2008.4586493
Event Name: 2008 American Control Conference
Event Place: Seattle, WA, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4976,
  title = {Computed Torque Control with Nonparametric Regression Models},
  author = {Nguyen-Tuong, D. and Seeger, M. and Peters, J.},
  journal = {Proceedings of the 2008 American Control Conference (ACC 2008)},
  booktitle = {ACC 2008},
  pages = {212-217},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = jun,
  year = {2008},
  month_numeric = {6}
}