Papers
21
Total Citations
619
H-Index
11
About
Eric Heiden is a robotics and AI researcher whose work spans autonomous robot navigation, differentiable simulation, and motion planning. He is perhaps best known for his contributions to the DARPA Subterranean Challenge as part of TEAM CoSTAR, where his work on the NeBula autonomy framework and the LAMP mapping system helped the team achieve breakthrough results in robotic exploration of hazardous underground environments. These projects, garnering over 100 and 180 citations respectively, tackled some of the hardest challenges in SLAM and multi-robot coordination under perceptually degraded conditions. Beyond subterranean robotics, Heiden has made significant contributions to differentiable simulation — developing frameworks that allow physical contact dynamics to be optimized via gradients, enabling more efficient robot learning and grasp synthesis. His Grasp'D and Fast-Grasp'D papers demonstrated how differentiable contact modeling can dramatically improve dexterous manipulation. His earlier work on gradient-informed path smoothing and motion planning benchmarking further reflects a consistent commitment to making robot planning more principled and reproducible. With a research portfolio bridging simulation, learning, and real-world deployment, Heiden's work represents a compelling effort to close the gap between theoretical robotics and robust autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots53 citations · 2021
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- 6Interactive Differentiable Simulation35 citations · 2019
- 7Gradient-Informed Path Smoothing for Wheeled Mobile Robots30 citations · 2018
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