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.

News: Starting this fall 2023, I will be joining the Ira A. Fulton Schools of Engineering at Arizona State University as a tenure-track assistant professor.

News: This website will not be regularly maintained. Please visit the ASU lab website for more: https://asu-iris.github.io/

News: Two Ph.D. positions are available in our lab! Please check Joining for more details. The positions will commence as early as Spring 2024 and are fully funded (tuition + monthly salary).


My Research

My research focuses on developing interactive intelligence, which makes robots to

  • seamlessly interact with humans, by understanding human intent, assisting with human decision-making, improving its intelligence through human intuitive interactions, toward safe and personalized human-robot relationship;

  • skillfully manipulating physical objects, by learning and planning with physical representations, touching and affecting objects with maximum dexterity while ensuring safety and controllability, toward human-level physical intelligence.

I develop my solutions for interactive intelligence by combining a theoretical understanding of system fundamentals with empirical learning from readily accessible data. Our approaches are positioned at the intersection of control/optimization (model-based) and machine learning (data-driven), leveraging the complementary benefits of both.

Highlighted Research and Publications

1. Multi-contact (Dexterous) Manipulation

Task-Driven Hybrid Model Reduction for Dexterous Manipulation
Wanxin Jin and Michael Posa
Submitted to IEEE Transactions on Robotics (T-RO), conditionally accepted, 2023
[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]

Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics
Shenao Zhang, Wanxin Jin, Zhaoran Wang
International Conference on Machine Learning (ICML), 2023
[PDF coming soon] / [Code coming soon] / [Video coming soon]

2. Robot Learning with Human-On-The-Loop

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, 2023
[PDF coming soon] / [Code coming soon] / [Video coming soon]

Enforcing Hard Constraints with Soft Barriers: Safe-driven Reinforcement Learning in Unknown Stochastic Environments
Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu
International Conference on Machine Learning (ICML), 2023
[PDF] / [Code coming soon] / [Video coming soon]