Papers
17
Total Citations
312
H-Index
9
About
Wyatt Ubellacker is a robotics researcher whose work spans autonomous motion planning, legged locomotion, humanoid teleoperation, and field robotics systems. His most influential contribution, "Interactive Multi-Modal Motion Planning With Branch Model Predictive Control" (2022, 72 citations), addresses one of autonomous robotics' core challenges: enabling robots and vehicles to reason about the unpredictable, multimodal behaviors of uncontrolled agents in shared environments. Alongside this, his work on online learning of unknown dynamics for legged robots (52 citations) demonstrates a sophisticated approach to closing the gap between idealized models and real-world physics, enabling more robust locomotion in practice. Ubellacker has also made notable contributions to humanoid robotics, co-developing the HERMES system—a full-body teleoperation platform with balance feedback (53 citations)—which advances the frontier of human-robot physical collaboration. His broader portfolio includes safe motion primitive verification, vision-based self-supervised safety control, and applied systems engineering for NASA's Mars 2020 mission. With over 280 total citations across diverse platforms—from underwater inspection robots to legged systems—Ubellacker represents a versatile and impactful voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1Interactive Multi-Modal Motion Planning With Branch Model Predictive Control72 citations · 2022
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- 7Software system for the Mars 2020 mission sampling and caching testbeds13 citations · 2018
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