Learning Adaptive Multi-Objective Robot Navigation Incorporating Demonstrations
<|reference_start|>Learning Adaptive Multi-Objective Robot Navigation Incorporating Demonstrations: Preference-aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing user feedback or demonstrations for personalization. However, personal preferences are subject to change and might even be context-dependent. Yet traditional reinforcement learning (RL) approaches with static reward functions often fall short in adapting to these varying user preferences, inevitably reflecting demonstrations once training is completed. This paper introduces a framework that combines multi-objective reinforcement learning (MORL) with demonstration-based learning. Our approach allows for dynamic adaptation to changing user preferences without retraining. It fluently modulates between reward-defined preference objectives and the amount of demonstration data reflection. Through rigorous evaluations, including a sim-to-real transfer on two robots, we demonstrate our framework's capability to reflect user preferences accurately while achieving high navigational performance in terms of collision avoidance and goal pursuance.<|reference_end|>
@article{de heuvel2024learning,
    title={Learning Adaptive Multi-Objective Robot Navigation Incorporating
  Demonstrations},
    author={Jorge de Heuvel, Tharun Sethuraman, Maren Bennewitz},
    journal={arXiv preprint arXiv:2404.04857},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04857},
    primaryClass={cs.RO}
}