Interactive robots have gained popularity, yet most are limited to motion, conversation, and recognition, lacking the ability to autonomously live and adapt like humans. This paper presents RoboLife, a novel system that empowers robots to perceive and learn from their environment, engage in human-like interactions with users and other robots, and autonomously plan activities. RoboLife introduces an adaptive framework with three core components: perception, human-life simulation, and dynamic learning. These enable robots to adapt, perform tasks, and engage meaningfully with their surroundings. Its key innovation lies in integrating environmental influences, human interactions, and inter-robot communication, allowing robots to form opinions, remember experiences, and autonomously execute activities---effectively living their own lives. The demo is available at https://youtu.be/uz6HtkDkXxY.

In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradually been used in drug research with strong computational capability and is further applied in anti-HBV drug screening, thus facilitating a reliable drug screening process. However, the lack of structural information in traditional compound analysis is an important hurdle for unsatisfactory efficiency in drug screening. Here, a natural language processing technique was adopted to analyze compound simplified molecular input line entry system strings. By using the targeted optimized word2vec model for pretraining, we can accurately represent the relationship between the compound and its substructure. The machine learning model based on training results can effectively predict the inhibitory effect of compounds on HBV and liver toxicity. The reliability of the model is verified by the results of wet-lab experiments. In addition, a tool has been published to predict potential compounds. Hence, this article provides a new perspective on the prediction of compound properties for anti-HBV drugs that can help improve hepatitis B diagnosis and further develop human health in the future.