Inverse optimal control from incomplete trajectory observations

Wanxin Jin, Dana Kulić, Shaoshuai Mou, Sandra Hirche

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

This article develops a methodology that enables learning an objective function of an optimal control system from incomplete trajectory observations. The objective function is assumed to be a weighted sum of features (or basis functions) with unknown weights, and the observed data is a segment of a trajectory of system states and inputs. The proposed technique introduces the concept of the recovery matrix to establish the relationship between any available segment of the trajectory and the weights of given candidate features. The rank of the recovery matrix indicates whether a subset of relevant features can be found among the candidate features and the corresponding weights can be learned from the segment data. The recovery matrix can be obtained iteratively and its rank non-decreasing property shows that additional observations may contribute to the objective learning. Based on the recovery matrix, a method for using incomplete trajectory observations to learn the weights of selected features is established, and an incremental inverse optimal control algorithm is developed by automatically finding the minimal required observation. The effectiveness of the proposed method is demonstrated on a linear quadratic regulator system and a simulated robot manipulator.

Original languageEnglish
Pages (from-to)848-865
Number of pages18
JournalInternational Journal of Robotics Research
Volume40
Issue number6-7
DOIs
StatePublished - Jun 2021

Keywords

  • Inverse optimal control
  • incomplete trajectory observations
  • inverse reinforcement learning
  • objective learning

Fingerprint

Dive into the research topics of 'Inverse optimal control from incomplete trajectory observations'. Together they form a unique fingerprint.

Cite this