Papers
3
Total Citations
124
H-Index
3
About
Yannick Schnider is a robotics researcher whose work bridges the gap between soft robotics, neuromorphic vision, and autonomous navigation in complex natural environments. His most impactful contribution, “Soft Robot Control With a Learned Differentiable Model” (2020, 95 citations), pioneered a data-driven approach to modeling and controlling soft robots—machines that are inherently safe and compliant, making them ideal for search-and-rescue and medical applications where uncertainty and human interaction are high. Schnider also advanced neuromorphic computing with his 2023 paper on optical flow, demonstrating how event cameras can enable efficient, real-time motion perception for edge devices and robots. Most recently, his 2024 work on learning occluded branch depth maps in forests addresses a critical challenge for aerial robots navigating dense vegetation, supporting applications in environmental monitoring and agriculture. Across these contributions, Schnider consistently tackles the tension between computational efficiency and real-world robustness, making him a key figure in the development of perceptive, adaptable robots for unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Soft Robot Control With a Learned Differentiable Model95 citations · 2020
- 2Neuromorphic Optical Flow and Real-time Implementation with Event Cameras20 citations · 2023
- 3