Robust Learning from Demonstrations with Mixed Qualities Using Leveraged Gaussian Processes

Sungjoon Choi, Kyungjae Lee, Songhwai Oh

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


In this paper, we focus on the problem of learning from demonstration (LfD) where demonstrations with different proficiencies are provided without labeling. To this end, we model multiple policies with different qualities as correlated Gaussian processes and present a leverage optimization method that estimates the leverage of each policy where the difference between two leverages defines the correlation between the corresponding policies. To recover a single policy function of an expert, we present a sparsity constraint on the leverage parameters. We first show that the proposed leverage optimization method can recover the correlations between sensory fields where the fields are realized from correlated Gaussian processes and sensor measurements are collected from the fields. Furthermore, we applied the proposed method to autonomous driving experiments, where demonstrations are collected from three different driving modes. While the driving policies are not realized from correlated processes, the proposed method assigns reasonable leverages to the driving demonstrations. The estimated driving policy of an expert, which incorporates the optimized leverages, outperforms previous LfD methods in terms of both safety and driving quality.

Original languageEnglish
Article number8626460
Pages (from-to)564-576
Number of pages13
JournalIEEE Transactions on Robotics
Issue number3
Publication statusPublished - 2019 Jun
Externally publishedYes


  • Autonomous navigation
  • learning from demonstration (LfD)
  • leveraged Gaussian processes (LGPs)
  • robust estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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