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Resilient odometry via hierarchical adaptation

AnyThermal: Towards Learning Universal Representations for Thermal Perception

Sebastian Scherer

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 “resilient robotics” 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 “grounding” 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 “cleaning up” 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.

Intelligent UAVs

Computer Vision

AI Reasoning for Robotics

Multisensor Data Fusion

Aerial Robotics

Motion Planning

Robotics Foundations

3-D Vision and Recognition

Air Lab

Vehicle operator workload estimation system and method

Associate Research Professor

ICRA'25 Towards Reliable & Trustworthy Embodied AI in Everyday Scenarios (RT-eAI) Workshop

Invited Talk: The Safety Case for Embodied Intelligence

Speaker: Sebastian Scherer, Carnegie Mellon University

Short Bio: Dr. Sebastian Scherer is the Director of Fieldable Embodied AI at Field AI and Associate Research Professor at Carnegie Mellon University's Robotics Institute. A globally-renowned expert, his groundbreaking research has redefined what is possible in deployable AI, with a focus on creating safe, adaptive systems for embodied intelligence in uncertain and dynamic environments. Dr. Scherer’s pioneering research has driven major advancements in field robotics, from autonomous landing systems to obstacle avoidance and mobility, influencing both academic and industry standards. His groundbreaking work continues to shape the future of AI by enabling scalable, high-performance systems capable of safely navigating real-world challenges.

Workshop Website: https://sites.google.com/view/reliable-embodied-ai/

[ICRA'25 RT-eAI Workshop] Sebastian Scherer: The Safety Case for Embodied Intelligence

On a monthly basis, we invite top-level AI researchers to give us a glimpse into their work and the future of AI. 

We are very happy to welcome Prof. Sebastian Scherer from Carnegie Mellon University, who will give a lecture on "Multi-Modal and Multi-Robot Coordination in Challenging Environments".

Abstract:
Robustness show great promise if they can get out of the lab into the field and go beyond a single-operator per robot paradigm. However, the unstructured nature of the real-world requires nuanced decision making of the robot.

In this talk I will outline some of our approaches, progress, and results on multi-modal sensing, providing nuanced perception inputs, as well as navigation in difficult terrain, and extensions to multi-robot teams, and future directions of our research.

Bio: 
Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI) at Carnegie Mellon University (CMU). His research focuses on enabling autonomy in challenging environments and previously led CMU’s entry to the SubT challenge. He and his team have shown several firsts for autonomy for flying robots and off-road driving .

Dr. Scherer received his B.S. in Computer Science, M.S. and Ph.D. in Robotics from CMU in 2004, 2007, and 2010.

Munich AI Lectures: Sebastian Scherer

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