Towards Multi-Goal Navigation in the Real-World Using Reinforcement Learning
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Date
2023-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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Distribution Rights granted to UMBC by the author.
Distribution Rights granted to UMBC by the author.
Abstract
Robots have proven to be highly effective in carrying out tasks requiring precise execution. However, when faced with real-world environments featuring sparse rewards and multiple goals, learning becomes a significant challenge, and Reinforcement Learning (RL) algorithms often struggle to acquire optimal policies. A common approach is to train in simulation environments and then fine-tune in the real world, although adapting to the real-world context remains challenging. This thesis introduces a novel method that addresses the challenge Sim2Real transfer of multi-goal navigation through reinforcement learning. The method utilizes object detectors as a pre-processing step to facilitate multi-goal navigation and transfer the learned knowledge to real-world scenarios. Empirical results demonstrate that the proposed method surpasses state-of-the-art baselines in terms of training time and overall performance, both in simulation environments and real-world settings. Although both methods achieve a perfect 100% success rate in simple environments, when it comes to single goal-based navigation, the proposed method demonstrates superior performance compared to the baseline in more challenging scenarios. Specifically, in complex environments and multi-goal scenarios within the AirSim environment, the proposed method surpasses the baseline by 18% and 5% respectively. As a proof of concept and to showcase its versatility, we also tested the approach in a low-fidelity environment, where it achieved similar levels of performance. To demonstrate the real-world implementation and validate the proposed method, we deployed it on a nano-drone named Crazyflie and a Jetbot with a front camera to conduct multi-goal navigation experiments. This practical deployment serves as a proof of concept, highlighting the effectiveness of the proposed method in real-world scenarios.