ReProHRL: Towards Multi-Goal Navigation in the Real World using
  Hierarchical Agents
Tejaswini Manjunath Mozhgan Navardi Prakhar Dixit Bharat Prakash Tinoosh Mohsenin
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, we deploy the
proposed method on a nano-drone named Crazyflie with a front camera to perform
multi-goal navigation experiments.