Robot Program Parameter Inference via Differentiable Shadow Program Inversion

@article{Alt2021RobotPP,
  title={Robot Program Parameter Inference via Differentiable Shadow Program Inversion},
  author={Bastian Alt and Darko Katic and Rainer J{\"a}kel and Asil Kaan Bozcuoğlu and Michael Beetz},
  journal={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={4672-4678}
}
  • Bastian AltDarko Katic M. Beetz
  • Published 26 March 2021
  • Computer Science
  • 2021 IEEE International Conference on Robotics and Automation (ICRA)
Challenging manipulation tasks can be solved effectively by combining individual robot skills, which must be parameterized for the concrete physical environment and task at hand. This is time-consuming and difficult for human programmers, particularly for force-controlled skills. To this end, we present Shadow Program Inversion (SPI), a novel approach to infer optimal skill parameters directly from data. SPI leverages unsupervised learning to train an auxiliary differentiable program… 

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