A Planning Approach with Learned Ensemble ModelsDownload PDF


Nov 16, 2020 (edited Jan 27, 2021)RCC 2020 Challenge Blind SubmissionReaders: Everyone
  • Keywords: Planning, Model learning
  • TL;DR: Real-Time , Zero-Order Optimization with Learned Models
  • Abstract: For the real robot challenge, we combine a fast zero-order optimization method (iCEM) with model learning. Our iCEM method generates strong expert trajectories using a learned forward dynamics model, which is iteratively trained on newly collected data. We use parallel forward model ensembles and use their uncertainty as a cost penalty in the optimization. Through the tight coupling between all components, our algorithm runs in real-time, rendering it suitable for real robotic applications.
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