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ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents by Manjunath, Tejaswini; Navardi, Mozhgan; Dixit, Prakhar; Prakash, Bharat; Mohsenin, Tinoosh is a scholarly article available to read on EtoBox.

What is ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents about?

Robots have been successfully used to perform tasks with high precision. In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and Reinforcement Learning (RL) algorithms fail to learn good policies. Training in simulation environments and then fine-tuning in the real world is a common approach. However, adapting to the real-world setting is a challenge. In this paper, we present a method named Ready for Production Hierarchical RL (ReProHRL) that divides tasks with hierarchical multi-goal navigation guided by reinforcement learning. We also use object detectors as a pre-processing step to learn multi-goal navigation and transfer it to the real world. Empirical results show that the proposed ReProHRL method outperforms the state-of-the-art baseline in simulation and real-world environments in terms of both training time and performance. Although both methods achieve a 100% success rate in a simple environment for single goal-based navigation, in a more complex environment and multi-goal setting, the proposed method outperforms the baseline by 18% and 5%, respectively. For the real-world implementation and proof of concept demonstration

Author

Manjunath, Tejaswini; Navardi, Mozhgan; Dixit, Prakhar; Prakash, Bharat; Mohsenin, Tinoosh

Published

2023

Language

EN