Roll-Drop: accounting for observation noise with a single parameter

• URL: http://arxiv.org/abs/2304.13150v1
• Date: Tue, 25 Apr 2023 20:52:51 GMT
• Title: Roll-Drop: accounting for observation noise with a single parameter
• Authors: Luigi Campanaro and Daniele De Martini and Siddhant Gangapurwala and Wolfgang Merkt and Ioannis Havoutis
• Abstract summary: This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) It uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state. We demonstrate an 80% success rate when up to 25% noise is injected in the observations, with twice higher robustness than the baselines.
• Score: 15.644420658691411
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state. DRL is a promising approach to control robots for highly dynamic and feedback-based manoeuvres, and accurate simulators are crucial to providing cheap and abundant data to learn the desired behaviour. Nevertheless, the simulated data are noiseless and generally show a distributional shift that challenges the deployment on real machines where sensor readings are affected by noise. The standard solution is modelling the latter and injecting it during training; while this requires a thorough system identification, Roll-Drop enhances the robustness to sensor noise by tuning only a single parameter. We demonstrate an 80% success rate when up to 25% noise is injected in the observations, with twice higher robustness than the baselines. We deploy the controller trained in simulation on a Unitree A1 platform and assess this improved robustness on the physical system.

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