On socially compliant navigation: Researchers show how real-world RL-based finetuning can enable mobile robots to adapt on the fly to the behavior of humans, to obstacles, and other challenges associated with real-world navigation:
Abstract.
We propose an online reinforcement learning approach, SELFI, to fine-tune a control policy trained on model-based learning. In SELFI, we combine the best parts of data efficient model-based learning with flexible model-free reinforcement learning, alleviating both of their limitations. We formulate a combined objective: the objective of the model-based learning and the learned Q-value from model-free reinforcement learning. By maximizing this combined objective in the online learning process, we improve the performance of the pre-trained policy in a stable manner. Main takeaways from our method are.
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