Robotics Research

The depth and breadth of IRIM breaks through disciplinary boundaries and allows for transformative research that transitions from theory to robustly deployed systems featuring next-generation robots. Fundamental research includes expertise in mechanics, control, perception, artificial intelligence and cognition, interaction, and systems. Our strategic research is organized around six main themes.

Core Research Facilities

The Institute for Robotics and Intelligent Machines at Georgia Tech supports and facilitates the operation of several core research facilities on campus allowing our faculty, students and collaborators to advance the boundaries of robotics research.

Robotics Education

Georgia Tech offers an interdisciplinary path to an MS in Robotics, as well as the first Ph.D. program in robotics, to students enrolled in a participating school within either the College of Computing or the College of Engineering. A fully integrated, multidisciplinary experience, the M.S. & Ph.D. programs include both coursework and research with faculty members in various units across campus.

IRIM & Outreach

The Institute for Robotics & Intelligent Machines (IRIM) participates in numerous K-12 STEM and community outreach activities related to robotics. Additionally, IRIM hosts tours throughout the year, and our student group, RoboGrads, participates in activities to raise awareness of the importance of robotics technology and stimulate interest in the field

IRIM & Industry

Our Industry Affiliates Program allows members to explore opportunities for research collaboration, facilities and services, consulting, student hiring, and other interactions. Whether you join as a strategic partner, an affiliate, or as a member of one of our customized consortia, your company will be supported through our work as a interdisciplinary group of robotics leaders.

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Robot Motion Planning: Challenges and Opportunities for Increasing Robot Autonomy


Featuring Mark Moll, Ph.D.| Director of Research; PickNik Robotics
January 26, 2022 | 12:15PM - 1:15PM | Marcus Nanotechnology Building 1116-1118

In this presentation I will first give a brief overview of sampling-based motion planning, a class of methods that has been successfully applied to a broad range of complex systems. I will present recent results that show that satisfying hard constraints can be decoupled from the particular planning strategy, which can lead to surprising performance improvements. Next, I will present some results on using hyperparameter optimization to select and tune motion planning algorithms for a given robot. Finally, will present some initial results on supervised autonomy that combines motion planning with compliant control, perception, and human input.