Christopher Thierauf, PhD

Tag: Resilience

Posts about how robots can make decisions that improves overall task performance, even when failure occurs, or that can avoid the failure in the first place.

Self-Debugging Robots: Fault recovery through reasoning and planning.

Christopher Thierauf, Matthias Scheutz. IEEE Resilience Week 2025.

Can robots reason about what actions to take to better understand failure? I show a technique and demonstrate how this is possible.
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December 3, 2024
Fixing Symbolic Plans with Reinforcement Learning in Object-Based Action Spaces.

Christopher Thierauf, Matthias Scheutz. IEEE IROS 2024.

We can redesign the typical reinforcement learning pipeline to train faster and integrate with symbolic plans by training on the environment directly, not the robot within it.

You can read the paper here.
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October 14, 2024
“Do This Instead”—Robots That Adequately Respond to Corrected Instructions.

Christopher Thierauf, Ravenna Thielstrom, Bradley Oosterveld, Will Becker, Matthias Scheutz. ACM Transactions on HRI.

How can robots handle self-corrections in human commands?
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August 26, 2024
Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots.

Christopher Thierauf, Theresa Law, Tyler Frasca, Matthias Scheutz. MDPI Machines 2024.

How can robots use dialog to explain, understand, and resolve failure?

You can read the full paper here.
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January 14, 2024