Physical AI Safety

Physical AI systems such as robots and humanoids are increasingly capable, yet deploying them safely around humans remains an open challenge. This raises the following question.

How do we build physical AI that are reliably safe?

Keywords

  • Vision-Language-Action Models
  • Reinforcement Learning
  • Conformal Abstention
  • Conformal Prediction
  • Uncertainty Quantification
Minjae Lee* , Yoonjae Jung* , Sangdon Park
2025
🏆 Best Paper Finalist from CKAIA
Haoran Wang , Zheng Yang , Sangdon Park , Yibin Yang , Seulbae Kim , Willian Lunardi , Martin Andreoni , Taesoo Kim , Wenke Lee
DSN 2025
Ramneet Kaur , Kaustubh Sridhar , Sangdon Park , Yahan Yang , Susmit Jha , Anirban Roy , Oleg Sokolsky , Insup Lee
ICCPS 2023
🏆 ICCPS'23 Best Paper Award Finalist
Sooyong Jang , Sangdon Park , Insup Lee , Osbert Bastani
ICML 2022
Shuo Li , Sangdon Park , Xiayan Ji , Insup Lee , Osbert Bastani
2022
Ramneet Kaur , Susmit Jha , Anirban Roy , Sangdon Park , Edgar Dobriban , Oleg Sokolsky , Insup Lee
AAAI 2021
Jean Oh , Thomas M Howard , Matthew R Walter , Daniel Barber , Menglong Zhu , Sangdon Park , Arne Suppe , Luis Navarro-Serment , Felix Duvallet , Abdeslam Boularias , others
ISER 2016
Sangdon Park , Wonsik Kim , Kyoung Mu Lee
ECCV 2012