Project Description

Robots (especially humanoid robots) hold immense promise for disaster response, home assistance, space exploration, and manufacturing. These robots, when equipped with intelligent interaction capabilities, can effectively collaborate with humans in various settings, such as warehouses and military missions. While significant advances have been made in enabling robots to navigate complex environments using both physics-based and data-driven approaches, the development of intelligent and physically safe human-robot interactions remains an unresolved challenge. This challenge is primarily due to two factors: the highly complex dynamical behaviors involved in physical human-robot interaction and the intricate cognitive behaviors of humans, such as trust towards robots and preferences in their usage, which are both difficult to model and predict.

Motivated by these practical demands and research gaps, this project aims to integrate model-based and data-driven approaches to facilitate intelligent interactions between humans and autonomous robots. The project will enhance our understanding of human trust and preferences in physical collaboration with autonomous robots. Building on this understanding, we will develop novel learning control schemes that foster appropriate levels of trust and accommodate human preferences. Experimental validation will be conducted using a Digit humanoid robot available in Dr. Gu’s lab.

Start Date

Spring or Summer of 2025 

Postdoc Qualifications

Candidates with a strong background and interest in more than one of the following areas are desired: Robotics, especially humanoid robots and/or robotic manipulators.
Controls, Optimization, and Learning.
Hands-on and coding experience is preferred (not required).

Co-advisors

Yan Gu (primary)
School of Mechanical Engineering
yangu@purdue.edu
https://www.thetracelab.com/

Shaoshuai Mou (second)
School of Aeronautics and Astronautics
mous@purdue.edu
https://engineering.purdue.edu/AIMS

Bibliography

1. Iqbal A, Gao Y, Gu Y. Provably stabilizing controllers for quadrupedal robot locomotion on dynamic rigid platforms. IEEE/ASME Transactions on Mechatronics. 2020 Jun 4;25(4):2035-44.
2. Jin W, Kulić D, Mou S, Hirche S. Inverse optimal control from incomplete trajectory observations. The International Journal of Robotics Research. 2021 Jun;40(6-7):848-65.
3. Gao Y, Yuan C, Gu Y. Invariant filtering for legged humanoid locomotion on a dynamic rigid surface. IEEE/ASME Transactions on Mechatronics. 2022 Aug 2;27(4):1900-9.
4. Jin W, Murphey TD, Kulić D, Ezer N, Mou S. Learning from sparse demonstrations. IEEE Transactions on Robotics. 2022 Aug 3;39(1):645-64.
5. Iqbal A, Veer S, Gu Y. Analytical solution to a time-varying LIP model for quadrupedal walking on a vertically oscillating surface. Mechatronics. 2023 Dec 1;96:103073.