Robin Schmid — accepted AI-conference papers

2024

Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition

Edoardo Debenedetti, Javier Rando, Daniel Paleka, Silaghi Fineas Florin, Dragos Albastroiu, Niv Cohen +15

NeurIPS 2024spotlight

Large language model systems face significant security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study this problem, we organized a capture-the-flag competition at IEEE SaTML 2024, where the flag is a secret string in th…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗

2024

MassSpecGym: A benchmark for the discovery and identification of molecules

Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich +24

NeurIPS 2024spotlight

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2022

PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains

Sharmita Dey, David Fan, Robin Schmid, Anushri Dixit, Kyohei Otsu, Thomas Touma +2

IROS 2022poster

Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to mor…

Cited by 9 Source ↗Scholar ↗

2022

Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain

Robin Schmid, Deegan Atha, Frederik Schöller, Sharmita Dey, Seyed Fakoorian, Kyohei Otsu +5

IROS 2022poster

Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert. This requires a si…

Cited by 43 Source ↗Scholar ↗