The natural interaction between robots and pedestrians in the process of autonomous navigation is crucial for the intelligent development of mobile robots, which requires robots to fully consider social rules and guarantee the psychological comfort of pedestrians. Among the research results in the field of robotic path planning, the learning-based socially adaptive algorithms have performed well in some specific human-robot interaction environments. However, human-robot interaction scenarios are diverse and constantly changing in daily life, and the generalization of robot socially adaptive path planning remains to be further investigated. In order to address this issue, this work proposes a new socially adaptive path planning algorithm by combining the generative adversarial network (GAN) with the Optimal Rapidly-exploring Random Tree (RRT*) navigation algorithm. Firstly, a GAN model with strong generalization performance is proposed to adapt the navigation algorithm to more scenarios. Secondly, a GAN model based Optimal Rapidly-exploring Random Tree navigation algorithm (GAN-RRT*) is proposed to generate paths in human-robot interaction environments. Finally, we propose a socially adaptive path planning framework named GAN-RTIRL, which combines the GAN model with Rapidly-exploring random Trees Inverse Reinforcement Learning (RTIRL) to improve the homotopy rate between planned and demonstration paths. In the GAN-RTIRL framework, the GAN-RRT* path planner can update the GAN model from the demonstration path. In this way, the robot can generate more anthropomorphic paths in human-robot interaction environments and has stronger generalization in more complex environments. Experimental results reveal that our proposed method can effectively improve the anthropomorphic degree of robot motion planning and the homotopy rate between planned and demonstration paths.

Background: Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings. Results: In this work, we propose KGML-xDTD: a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases. It is a two-module framework that not only predicts the treatment probabilities between drugs/compounds and diseases but also biologically explains them via knowledge graph (KG) path-based, testable mechanisms of action (MOAs). We leverage knowledge-and-publication based information to extract biologically meaningful "demonstration paths" as the intermediate guidance in the Graph-based Reinforcement Learning (GRL) path-finding process. Comprehensive experiments and case study analyses show that the proposed framework can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths. Conclusions: KGML-xDTD is the first model framework that can offer KG-path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications. We believe it can effectively reduce "black-box" concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations, and further accelerate the process of drug discovery for emerging diseases.