好色先生TV

Expertise

Topics:聽 Intelligent UAVs, Computer Vision, AI Reasoning for Robotics, Multisensor Data Fusion, Aerial Robotics, Motion Planning, Robotics Foundations, 3-D Vision and Recognition

Over the last decade in my lab, I have achieved resilient performance of robots by advancing the robustness, redundancy, and resourcefulness of the algorithms as well as systems. While careful engineering is part of resilient performance, I am particularly interested in answering the following fundamental questions:

-How does one design algorithms and systems that are robust in the face of large uncertainty and learning-based components?
-Where can we inject redundancy into the system without incurring excessive computation or weight penalties?
-How can we move beyond fixed behaviors, policies, or interpretations of the data and have a continuous improvement of our systems to achieve resiliency in the face of large uncertainty with little data?

For more than a decade, I have made fundamental contributions to this new area of 鈥渞esilient robotics鈥� to answer those key research questions for SLAM, perception, and planning, by demonstrating pioneering results, as well as by evaluating the resilience in the context of applications such as subterranean exploration, search & rescue, triage, wildfire, safety in shared airspace, autonomous offroad driving, autonomous full-scale helicopter flight, bridge inspection, and drone delivery.

I explore resilient robotics by 鈥済rounding鈥� research problems in impactful applications. My efforts are not limited by what is perceived as too difficult or too laborious. I embrace the challenge and build as necessary and leverage what already exists if possible. A large part of my effort goes into formulating what the core research problem is and then 鈥渃leaning up鈥� these problems. Often, I find that existing problem formulations addressed in prior work have a fundamental gap in their assumptions to be able to be effective for relevant applications which require advancements in core methods. I strive to validate our methods in the field in closed-loop experiments, beyond benchmarking on datasets. I test early and test often since I have seen that these experiences lead to richer feedback for the systems, and as I gather more data, algorithms keep improving. It is now an exciting time since I can go beyond relying on smart engineering of solutions and can start making stronger assertions using large-scale evaluations.