Papers
7
Total Citations
131
H-Index
4
About
Julius Jankowski is a robotics researcher whose work spans robot perception, motion planning, and learning from demonstration, with a particular focus on enabling robots to operate safely and adaptively alongside humans. His most widely recognized contribution, "Sliding Mode Momentum Observers for Estimation of External Torques and Joint Acceleration" (2019, 79 citations), introduced robust methods for detecting collisions and estimating external forces in human-robot interaction — a foundational capability for safe collaborative robotics. Building on this, Jankowski has made significant strides in trajectory optimization, most notably through his VP-STO framework (2023, 32 citations), which leverages via-point-based stochastic optimization to enable fast, reactive replanning in dynamic environments. His research also advances learning from demonstration, developing probabilistic adaptive control strategies and movement primitives that allow robots to generalize skills from sparse human guidance, reducing the burden on users while maintaining robust performance. His 2020 work on hierarchical motion planning further demonstrates his systems-level thinking, integrating safety and autonomy for industrial human-robot collaboration. With over 130 citations across his published work, Jankowski represents an emerging voice in intelligent, human-centered robot autonomy.
Research Focus
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Top Papers
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- 6Probabilistic Adaptive Control for Robust Behavior Imitation2 citations · 2021
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