IRRL lets robots learn social navigation in the real world by incrementally updating only the differences from a base policy, matching replay-buffer methods in simulation and adapting to new settings on physical robots.
Rupam Mahmood, ”Asynchronous reinforcement learning for real-time control of physical robots,” inProceedings of the International Conference on Robotics and Automation (ICRA), pp
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Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation
IRRL lets robots learn social navigation in the real world by incrementally updating only the differences from a base policy, matching replay-buffer methods in simulation and adapting to new settings on physical robots.