Chris Thierauf
, PhD
Ocean Robotics and Autonomy Researcher
I’m a Research Engineer at the Woods Hole Oceanographic Institution (WHOI).
I research autonomous partners for scientific exploration of challenging domains. I mostly work with and help deploy the full-ocean depth Sentry AUV.
Select Updates
publication
september 2026
Deployments of a Deep Submergence Gravimeter
The first continuous-operation deep-sea gravimeter on an AUV. IEEE OCEANS.
publication
september 2026
Visual Mission Composition for Teleoreactive AUVs
HRI and cognitive science principles inform a mission planning UI for real-world autonomy. IEEE OCEANS.
fieldwork
may 2026
Wake Atoll Fieldwork
Sentry photogrammetry surveys with AUV Sentry aboard the R/V Nautilus.
publication
may 2026
What Matters for Designing Resilient Systems?
I presented work on a new taxonomy on failure at IEEE ERAS 2026, in Zagreb, Croatia.
public speaking
march 2026
Bard College Talk: Deep Sea Autonomous Partners
I presented recent work in a Bard College seminar, with some notes here.
fieldwork
march 2026
Mariana Trench/Jurassic Quiet Zone Fieldwork
I went to sea with Sentry to perform magnetometry and gravimetry surveys of some of the oldest geologic formations on earth, which informs modern interpretations of that data.
writing
january 2026
Symbolic Planning: A Gentle Introduction
An approachable introduction to symbolic planning, symbolic AI, and the formal notation used. Aimed at graduate students, advanced undergraduates, or professionals new to the niche. Also, code.
publication
october 2025
A Cognitive Teleoreactive Mission Executive
A robot autonomy architecture based on cognitive architectures and long horizon planning principles, deployed on-board Sentry. IEEE OCEANS.
publication
october 2025
Depth-Triggered Mechanical Fuses
Mechanical fuses triggered by depth, allowing ballast drop with no electronic systems. Engineering work by Ethan Rowe, published at OCEANS.
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publication
september 2025
Solving Symbolic Novelties with Self-Generated RL
Robots that can generate reinforcement learning simulations to solve novelties.
publication
2024
Self-Debugging Robots for fault recovery
Robots that use reasoning and planning for hypothesizing the solutions to their own problems.
publication
2024
Fixing symbolic plans with reinforcement learning
Introduction of object-based observation and action spaces for symbolic plan repair.
About
I work in the Deep Submergence Laboratory (DSL) and National Deep Submergence Facility (NDSF) on the Sentry AUV. Previously, I was a grad student in the Tufts Human-Robot Interaction Lab, where I earned a joint PhD in Computer Science and Human-Robot Interaction.
I’m interested in a handful of research problems, all of which tie back to creating robots that are capable deep-sea exploration partners:
- How can robots make decisions on behalf of human operators? How do the operators know that these decisions are safe and in-line with human needs?
- How can robots communicate their observations and decisions when bandwidth is limited? We see this scenario both when a robot is deployed underwater with no tether, and when autonomous vehicles outpace human cognition.
- How can robots identify, interpret, and resolve failure autonomously? How can these new failure modes be identified and communicated?
- How can machine learning techniques (like the neural net systems that excel at identifying trends) be more tightly integrated into symbolic planning techniques (like the STRIPS or PDDL style planning systems that excel at long-horizon planning)?