Active Reward Learning from Online Preferences
This work addresses the challenge of enabling quick robot adaptation in critical real-world situations without overburdening human experts, though it is incremental in improving query efficiency.
The paper tackles the problem of adapting robot policies to human preferences and new environments by proposing an online active reward learning method that uses easy-to-answer pairwise preference queries, reducing the burden on human experts and outperforming baselines with fewer queries in simulation, human studies, and real robot experiments.
Robot policies need to adapt to human preferences and/or new environments. Human experts may have the domain knowledge required to help robots achieve this adaptation. However, existing works often require costly offline re-training on human feedback, and those feedback usually need to be frequent and too complex for the humans to reliably provide. To avoid placing undue burden on human experts and allow quick adaptation in critical real-world situations, we propose designing and sparingly presenting easy-to-answer pairwise action preference queries in an online fashion. Our approach designs queries and determines when to present them to maximize the expected value derived from the queries' information. We demonstrate our approach with experiments in simulation, human user studies, and real robot experiments. In these settings, our approach outperforms baseline techniques while presenting fewer queries to human experts. Experiment videos, code and appendices are found at https://sites.google.com/view/onlineactivepreferences.