Motion Planning with Success Judgement Model Based on Learning from Demonstration

Daichi Furuta, Kyo Kutsuzawa, Sho Sakaino, Toshiaki Tsuji

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


A technique named Learning from Demonstration allows robots to learn actions in a human living environment from the demonstrations directly. In a learning method from demonstrations directly, however, teaching actions cannot be reused between situations with different restrictions. In this study, we propose a method for training a success judgment model based on Learning from Demonstration and use this as a differentiable loss function of tasks. By formulating the constraints of the action in a manner in mathematical optimization and combining these constraints with the learned success judgment model into a loss function, an action generation model can be trained by the gradient method. This system was verified with the action of scooping up a pancake.

Original languageEnglish
Article number9064769
Pages (from-to)73142-73150
Number of pages9
JournalIEEE Access
Publication statusPublished - 2020


  • Force control
  • learning from demonstration
  • motion planning
  • robot learning


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