Wanxin Jin (靳万鑫)

Postdoctoral Researcher in the GRASP Lab, University of Pennsylvania.

I obtained my Ph.D. from the School of Aeronautics and Astronautics, Purdue University in 2021. I was a research assistant at Technical University of Munich, Germany, 2016-2017. I obtained my Master’s and Bachelor’s degrees in Control Science & Engineering from the Harbin Institute of Technology, China.


My Research

My research is to formalize and develop interactive autonomy, which characterizes robots’ capability of seamlessly interacting with humans, safely and skillfully touching and affecting physical objects. Specifically, I am making robots.

  • operate in cohort with humans, by understanding human intents, assisting human decisions, improving its autonomy through human intuitive interactions, toward safe and individualized human-robot relation;
  • reason about and manipulate physical objects, by learning and planning with physical representations, touching and affecting objects in a safe and controlled manner, toward human-like physical intelligence.

In an era of big data and deep models, I build my solutions to interactive autonomy by the integration of theoretically understanding the fundamentals/limits of the system and empirically learning from the most accessible data. Therefore, my methods of interactive autonomy lie at a middle ground that combines the complementary benefits of control/optimization (model-based) and machine learning (data-driven).

Highlighted Research and Publications

1. Dexterous Manipulation

Task-Driven Hybrid Model Reduction for Dexterous Manipulation
Wanxin Jin and Michael Posa
Submitted to IEEE Transactions on Robotics (T-RO), under review, 2022
[PDF]/ [Code]/ [Video]/ [Webpage]

Learning Linear Complementarity Systems
Wanxin Jin, Alp Aydinoglu, Mathew Halm, and Michael Posa
Learning for Dynamics and Control (L4DC), 2022
[PDF] / [Code]

2. Improving Robot Autonomy by Learning from the Most Efficient Human Interactions

Learning from Human Directional Corrections
Wanxin Jin, Todd D Murphey, and Shaoshuai Mou
IEEE Transactions on Robotics (T-RO), 2022
[PDF] / [Code] / [Videos]

Learning from Sparse Demonstrations
Wanxin Jin, Todd D Murphey, Dana Kulic, Neta Ezer, and Shaoshuai Mou
IEEE Transactions on Robotics (T-RO), 2022
[PDF]/ [Code] / [Videos]

Inverse Optimal Control from Incomplete Trajectory Observations
Wanxin Jin, Dana Kulic, Shaoshuai Mou, and Sandra Hirche
International Journal of Robotics Research (IJRR), 40:848–865, 2021
[PDF] / [Code]

Inverse Optimal Control for Multiphase cost functions
Wanxin Jin, Dana Kulic, Jonathan Lin, Shaoshuai Mou, and Sandra Hirche
IEEE Transactions on Robotics (T-RO), 35(6):1387–1398, 2019
[PDF] / [Code]

3. Fundamental Research: Bridging Control with Machine Learning

Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
Wanxin Jin, Zhaoran Wang, Zhuoran Yang, and Shaoshuai Mou
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF] / [Code] / [Videos]

Safe Pontryagin Differentiable Programming
Wanxin Jin, Shaoshuai Mou, and George J. Pappas
Advances in Neural Information Processing Systems (NeurIPS), 2021
[PDF] / [Code] / [Videos]

Robust Safe Learning and Control in Unknown Environments: An Uncertainty-Aware Control Barrier Function Approach
Jiacheng Li, Qingchen Liu, Wanxin Jin, Jiahu Qin, and Sandra Hirche
Submitted to IEEE Robotics and Automation Letters (RA-L), under review, 2022
[PDF coming soon] / [Code coming soon] / [Video coming soon]