Qianxu Wang

I am a first-year PhD student at Cornell University, advised by Prof. Kuan Fang. Previously, I was fortunate to work with Prof. Yixin Zhu at Peking University, Prof. Jeannette Bohg at Stanford, and Prof. Leonidas J. Guibas at Stanford.

My long-term research goal is to achieve human-level robust sensorimotor coordination in robotics. I am also very interested in 3D Vision and Animation. My previous research has primarily focused on dexterous manipulation from a semantic perspective.

Currently, I am thinking and exploring two key questions in manipulation:

What are the sources of knowledge for manipulation?

• Shared information across datasets. The features of Cross-embodiment, cross-environment, and cross-quality make robotic datasets unique compared to data in other fields like vision and natural language. Defining a universal data format and unifying existing datasets, rather than solely collecting new ones, presents a promising approach to fundamentally addressing data scarcity in robotics. I am eager to explore the structure of shared motion primitives and semantics in these datasets and investigate how to integrate them to achieve semantic-aware and robust manipulation in the real world.

• Shared foundations with scalable data sources. The vision domain offers rich semantic correspondences valuable for robotic perception, while natural language, as a natural carrier of reasoning and prompting, can enhance decision-making. I am excited to investigate the connections between robotics and scalable data sources by leveraging these shared foundations.

How can diverse sources of knowledge be effectively integrated?

• Structured Policy Design. Current policies (e.g. in IL/RL) directly map perception to actions of specific end-effector, which(i) process complex information without prioritization and (ii) limit the available data sources. In contrast, humans first reason about interactions visually, then adapt during manipulation using closed-loop feedback from multiple modalities (e.g., tactile, acoustic). I am excited to explore the design of a structured manipulation policy, including how to integrate the end-effector agnostic action representation from diverse data sources and when to incorporate multi-modal perception and close-loop control.

Feel free (and please do!) to reach out if you have any questions, comments about my research, or anything you’d like to discuss or share with me!