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Conference Papers Year : 2021

Delay Aware Universal Notice Network: Real world multi-robot transfer learning

Samuel Beaussant
  • Function : Author
Sebastien Lengagne
Benoit Thuilot
  • Function : Author

Abstract

General purpose simulators provide cheap training data to learn complex robotic skills. However, the transition from simulation to reality is often very challenging for the agent. One major issue is the delay on the physical robot that may deteriorate the performance of the deployed agent. Furthermore, once a successfully trained learning-based control policy is available, re-purposing the knowledge acquired by the agent to enable a structurally distinct agent to perform the same task is hazardous if done naively. In this work, we address the above issues with a single method, the DA-UNN (Delay Aware Universal Notice Network), which decomposes the knowledge into robot-specific and task-specific modules for fast transfer. Our framework deals with delays immanent to physical systems in order to improve sim2real transfer. We evaluate the efficiency of our approach using simulated and actual robots on a dynamic manipulation task where delay management is crucial.

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Dates and versions

hal-03383498 , version 1 (18-10-2021)

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Samuel Beaussant, Sebastien Lengagne, Benoit Thuilot, Olivier Stasse. Delay Aware Universal Notice Network: Real world multi-robot transfer learning. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Sep 2021, Prague, Czech Republic. ⟨10.1109/IROS51168.2021.9635917⟩. ⟨hal-03383498⟩
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