Research Directions

The IRIS lab focuses on three reserach directions: (1) human-robot alignment, (2) contact-rich robot manipulation, and (3) fundamental methods in robotics. Below are some recent publications in each set of research interest. Please visit Publications page for a full list of publications.

Human-robot alignment
We develop methods to empower a robot with the ability to efficiently understand and be understood by human users through a variety of physical interactions. We explore how robots can aptly respond to and collaborate meaningfully with users.
  • Robot learning from general human interactions
  • Planning and control for human-robot systems
Learning from Human Directional Corrections
Wanxin Jin, Todd D Murphey, Zehui Lu, and Shaoshuai Mou
IEEE Transactions on Robotics (T-RO), 2023
Paper Code Video
Learning from Sparse Demonstrations
Wanxin Jin, Todd D Murphey, Dana Kulic, Neta Ezer, and Shaoshuai Mou
IEEE Transactions on Robotics (T-RO), 2023
Paper Code Video
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
Paper 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
Paper Code
Contact-rich robot manipulation
We aim to leverage physical principles to develop efficient representations or models for robot's physical interaction with environments. We also focus on developing algorithms to enable robots efficiently and robustly manipulate their surroundings/objects through contact.

  • Learning, planning, and control for contact-rich manipulation
  • Computer vision and learnable geometry for dexterous manipulation
Task-Driven Hybrid Model Reduction for Dexterous Manipulation
Wanxin Jin and Michael Posa
IEEE Transactions on Robotics (T-RO), accepted, 2024
Paper Code Video Project
Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning
Wei-Cheng Huang, Alp Aydinoglu, Wanxin Jin, Michael Posa
submitted to IEEE International Conference on Robotics and Automation (ICRA), 2024
Paper Video Project
Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics
Shenao Zhang, Wanxin Jin, Zhaoran Wang
International Conference on Machine Learning (ICML), 2023
Paper
Learning Linear Complementarity Systems
Wanxin Jin, Alp Aydinoglu, Mathew Halm, and Michael Posa
Learning for Dynamics and Control (L4DC), 2022
Paper Code
Fundamental methods in robotics
We focus on developing fundamental theories and algorithms for achieving efficient, safe, and robust robot intelligence. Our methods lie at the intersection of model-based (control and optimization) and data-driven approaches, harnessing the complementary benefits of both.

  • Optimal control, motion plannig, reinforcement learning
  • Differentiable optimization, inverse optimization
  • Hybrid system learning and control
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
Paper Code Video Project
Safe Pontryagin Differentiable Programming
Wanxin Jin, Shaoshuai Mou, and George J. Pappas
Advances in Neural Information Processing Systems (NeurIPS), 2021
Paper Code Video Project
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
Paper Video
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
Paper

Software & Data

We embrace the open-source spirit and are committed to promoting research reproducibility and accessibility. Please find below some popular repositories we have highlighted. Please visit Publications or GitHub page for more.

Computation

Interactive Games

Manpulation