Intelligent Motion Laboratory at UIUC

About

The Intelligent Motion Lab is a "full stack" robotics lab that studies strategies for integrating planning, perception, and learning to enable autonomous and semi-autonomous operation in challenging tasks. The research conducted in the lab spans basic research on component algorithms up to application-driven projects on integrated robot systems. A key tenet of our research philosophy is that the fundamental ingredients of robotic intelligence are universal across embodiments and tasks, although their optimal instantiations may be specific. Applications of our research have been wide ranging, including agriculture, construction, intelligent vehicles, robot manipulation, legged locomotion, human-robot interaction, robot- and computer-assisted medicine.

IML is directed by Prof. Kris Hauser and is part of the University of Illinois Grainger College of Engineering.

IML is NOT accepting applications for new PhD students. Please see the UIUC Robotics Group website for more potential advisors.

Research Spotlight

EYESIGHT: a robot-stabilized retinal imaging system

Selected Projects

Modeling and reasoning about "stuff"

Summary (click to show)

Prevailing models in robotics reason about the world as a collection of rigid objects, but this is an inappropriate abstraction for manipulating cloth, ropes, piles of objects, plants, and natural terrain. We are investigating novel representations of "stuff" that are built de novo from visual and tactile perception data, whose properties are learned continuously through interaction. Volumetric Stiffness Fields, Graph Neural Networks, Neural Dynamics, and 3D metric-semantic maps are examples of models being investigated in this research. Applications are being studied in agriculture, construction, and household object manipulation.

deformable objects, graph neural networks, tactile sensing, agricultural robotics, construction robotics

NSF Foundational Robotics Research, USDA/NIFA AIFARMS AI Institute

Tele-nursing robots

Summary (click to show)

Our lab is developing the Tele-Robotic Intelligent Nursing Assistant (TRINA), a remote-controlled robot to perform common nursing duties inside hazardous clinical areas, which could reduce infection risk to healthcare workers by minimizing exposure to contagions and other biohazards. They could also reduce infection risk to immunocompromise patients. Apart from the engineering challenge, we are addressing scientific questions regarding human-robot interfaces, haptics, and semi-autonomous control and planning algorithms.

tele-nursing, assisted teleoperation, real-time planning, human-robot interaction

NSF NRI, NSF CAREER, NSF RAPID

Knowledge- and structure-driven motion planning

Summary (click to show)

The large motion planning problems that arise in robotics, CAD/CAM, animation of virtual characters, and surgical planning are very challenging: they require searching high-dimensional state spaces with complex geometric constraints, nonlinear dynamics, often with contact and impact, and long time horizons. Prior approaches to these problems either exploit problem structure and/or embed a great deal of domain knowledge by hand into a planner. But this process is tedious, error-prone, and does not scale well to harder problems whose structure is non-obvious. This project will investigate automated strategies for planning systems to automatically discover common solution structures from past experience and to reuse this knowledge in new problems.

theory, hybrid motion planning, optimal planning, locomotion

NSF Robust Intelligence Program

For a complete list, please see the research page.