### Abstract

Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as an unconstrained least-squares problem. We apply this approach to both simulated and experimental data to obtain a simple model of human walking trajectories. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.

Original language | English (US) |
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Title of host publication | 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 531-536 |

Number of pages | 6 |

ISBN (Print) | 9781467314039 |

DOIs | |

State | Published - Jan 1 2012 |

Event | 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 - Saint Paul, MN, United States Duration: May 14 2012 → May 18 2012 |

### Publication series

Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |

### Other

Other | 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 |
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Country | United States |

City | Saint Paul, MN |

Period | 5/14/12 → 5/18/12 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering

### Cite this

*2012 IEEE International Conference on Robotics and Automation, ICRA 2012*(pp. 531-536). [6225317] (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2012.6225317

**A convex approach to inverse optimal control and its application to modeling human locomotion.** / Puydupin-Jamin, Anne Sophie; Johnson, Miles; Bretl, Timothy Wolfe.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2012 IEEE International Conference on Robotics and Automation, ICRA 2012.*, 6225317, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 531-536, 2012 IEEE International Conference on Robotics and Automation, ICRA 2012, Saint Paul, MN, United States, 5/14/12. https://doi.org/10.1109/ICRA.2012.6225317

}

TY - GEN

T1 - A convex approach to inverse optimal control and its application to modeling human locomotion

AU - Puydupin-Jamin, Anne Sophie

AU - Johnson, Miles

AU - Bretl, Timothy Wolfe

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as an unconstrained least-squares problem. We apply this approach to both simulated and experimental data to obtain a simple model of human walking trajectories. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.

AB - Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as an unconstrained least-squares problem. We apply this approach to both simulated and experimental data to obtain a simple model of human walking trajectories. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.

UR - http://www.scopus.com/inward/record.url?scp=84864439219&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864439219&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2012.6225317

DO - 10.1109/ICRA.2012.6225317

M3 - Conference contribution

AN - SCOPUS:84864439219

SN - 9781467314039

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 531

EP - 536

BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012

PB - Institute of Electrical and Electronics Engineers Inc.

ER -