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Our Work

Research Theme:

Research on intelligent robotics lies at the intersection of machine learning and robotics, which aims at enhancing the level of autonomy for robots.

Applications include domestic robots, industrial robots, medical robots, warehouse robots, and mobile robots in unstructured environments.


  • providing tangible benefits in our daily life;

  • contributing to the healthcare system;

  • assisting humans in hazardous environments

The ultimate goal is to develop next-generation robots empowered by artificial generation intelligence with super-human capabilities.


Explainable Hierarchical Imitation Learning

Dandan Zhang; Qiang Li; Yu Zheng; Dongsheng Zhang; Zhengyou Zhang


To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. 
Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreover, with the EHIL method, a logical graph can be constructed for task execution, through which the decision-making process for action generation can be made explainable to users and the causes of failure can be traced out. Based on the logical graph, the framework is manipulable to achieve different targets while the adaptability to unseen scenarios can be achieved in an explainable manner.
A series of experiments have been conducted to verify the effectiveness of the proposed method. Results indicate that EHIL outperforms the traditional behavior cloning method in terms of success rate, adaptability, manipulability and explainability. 

Full paper link:

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