TechRxiv
safe_RL.pdf (285.11 kB)

Safe Deployment of a Reinforcement Learning Robot Using Self Stabilization

Download (285.11 kB)
preprint
posted on 25.06.2021, 21:29 by Nanda Kishore Sreenivas, Shrisha Rao
In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained. However, in robotics applications such as industrial systems or autonomous vehicles, this cannot be guaranteed. A robot can be pushed out of its training space by some unforeseen perturbation, which may cause it to go into an unknown state from which it has not been trained to move towards its goal. While most prior work in the area of RL safety focuses on ensuring safety in the training phase, this paper focuses on ensuring the safe deployment of a robot that has already been trained to operate within a safe space. This work defines a condition on the state and action spaces, that if satisfied, guarantees the robot's recovery to safety independently. We also propose a strategy and design that facilitate this recovery within a finite number of steps after perturbation. This is implemented and tested against a standard RL model, and the results indicate a much-improved performance.

History

Email Address of Submitting Author

shrao@ieee.org

ORCID of Submitting Author

0000-0003-0625-5103

Submitting Author's Institution

International Institute of Information Technology - Bangalore

Submitting Author's Country

India

Usage metrics

Licence

Exports