Papers
30
Total Citations
572
H-Index
12
About
Nadia Figueroa is a robotics researcher whose work spans motion planning, human-robot interaction, and learning from demonstration, with particular focus on making robotic systems safer, more reactive, and capable of operating alongside humans. Her most influential contribution — a unified framework for coordinated multi-arm motion planning (2018, 109 citations) — addressed the fundamental challenge of enabling multiple robotic arms to work in dynamic, uncertain environments, a problem she began tackling as early as 2016. Figueroa has made substantial contributions to collision avoidance, developing real-time self-collision avoidance methods for humanoid robots and pioneering neural implicit signed distance functions in joint space (53 citations) that allow manipulators to reason about collisions with unprecedented efficiency. Her work on dynamical systems-based motion planning, including locally active globally stable formulations, reflects a sustained commitment to motion policies that are both theoretically grounded and practically robust. She has also explored learning from demonstration through hierarchical frameworks applied to complex manipulation tasks like pizza dough rolling, bridging the gap between human skill and robot capability. With over 400 cumulative citations and contributions spanning soft robotics and provably safe human-robot interaction, Figueroa has established herself as a distinctive and wide-ranging voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1A unified framework for coordinated multi-arm motion planning109 citations · 2018
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- 4Real-Time Self-Collision Avoidance in Joint Space for Humanoid Robots49 citations · 2021
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- 7Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics32 citations · 2021
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