Jan Bruedigam — accepted AI-conference papers

2026

Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control

Albert H. Li, John Zhang, Jan Bruedigam, Brandon Hung, Aaron Ames, Jiuguang Wang +2

ICRA 2026poster

Sampling-based model predictive control (MPC) is experiencing a resurgence in robotics following both recent hardware successes and advancements in parallelized physics simulation. However, to build on this progress, the robotics community needs to develop shared tools for prototyping, benchmarking,…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation

Jan Bruedigam, Ali Adeeb Abbas, Maks Sorokin, Kuan Fang, Brandon Hung, Maya Guru +4

CoRL 2024poster

Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing planners are restricted to specif…

Cited by 2 Source ↗code ↗Scholar ↗