Zhou, Tianxing

Zhou, Tianxing

Ph.D.

Embodied AI Reinforcement Learning Human-Robot Interaction
Ph.D. Candidate (supervisor: Prof. Yu Chao & Prof. Yue Yufeng)
Zhongguancun Academy & School of Automation, Beijing Institude of technology | 2024-Present
B.Sc.
School of Automation, Beijing Institude of technology | 2020-2024

Welcome! I’m Zhou Tianxing (周天行), a first-year Ph.D. student in Control Science and Engineering at the School of Automation, Beijing Institude of Technology. I am fortunate to have Prof. Yue Yufeng as my supervisor. Currently, I am also undergoing joint training at Zhongguancun Academy, under the mentorship of Prof. Yu Chao.

My research interests currently focus on embodied AI and robotics. In the future, I will continue to deepen my work in embodied intelligence and robotics, promoting the seamless coexistence and interaction between humans and robots.

STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner

Tianxing Zhou, Zhirui Wang, Haojia Ao, Guangyan Chen, Boyang Xing, Jingwen Cheng, Yi Yang, Yufeng Yue

IROS 2025, 2025

The ability to perform reliable long-horizon task planning is crucial for deploying robots in real-world environments. However, directly employing Large Language Models (LLMs) as action sequence generators often results in low success rates due to their limited reasoning ability for long-horizon embodied tasks. In the STEP framework, we construct a subgoal tree through a pair of closed-loop models: a subgoal decomposition model and a leaf node termination model. Within this framework, we develop a hierarchical tree structure that spans from coarse to fine resolutions. The subgoal decomposition model leverages a foundation LLM to break down complex goals into manageable subgoals, thereby spanning the subgoal tree. The leaf node termination model provides real-time feedback based on environmental states, determining when to terminate the tree spanning and ensuring each leaf node can be directly converted into a primitive action. Experiments conducted in both the VirtualHome WAH-NL benchmark and on real robots demonstrate that STEP achieves long-horizon embodied task completion with success rates up to 34% (WAH-NL) and 25% (real robot) outperforming SOTA methods.

Human Demonstrations are Generalizable Knowledge for Robots

Te Cui*, Tianxing Zhou*, Mengxiao Hu, Haoyang Lu, Zicai Peng, Haizhou Li, Guangyan Chen, Meiling Wang, Yufeng Yue

IROS 2025, 2025

Learning from human demonstrations is an emerging trend for designing intelligent robotic systems. However, previous methods typically regard videos as instructions, simply dividing them into action sequences for robotic repetition, which poses obstacles to generalization to diverse tasks or object instances. In this paper, we propose a different perspective, considering human demonstration videos not as mere instructions, but as a source of knowledge for robots. Motivated by this perspective and the remarkable comprehension and generalization capabilities exhibited by large language models (LLMs), we propose DigKnow, a method that DIstills Generalizable KNOWledge with a hierarchical structure. Specifically, DigKnow begins by converting human demonstration video frames into observation knowledge. This knowledge is then subjected to analysis to extract human action knowledge and further distilled into pattern knowledge compassing task and object instances, resulting in the acquisition of generalizable knowledge with a hierarchical structure. In settings with different tasks or object instances, DigKnow retrieves relevant knowledge for the current task and object instances. Subsequently, the LLM-based planner conducts planning based on the retrieved knowledge, and the policy executes actions in line with the plan to achieve the designated task. Utilizing the retrieved knowledge, we validate and rectify planning and execution outcomes, resulting in a substantial enhancement of the success rate. Experimental results across a range of tasks and scenes demonstrate the effectiveness of this approach in facilitating real-world robots to accomplish tasks with the knowledge derived from human demonstrations.