4 papers
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
John Z. Zhang, Maks Sorokin, Jan Brüdigam +14
This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by perfor…
Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation
Lara Brudermüller, Brandon Hung, Xinghao Zhu +4
We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models tr…
Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
Albert H. Li, Brandon Hung, Aaron D. Ames +3
Recent advancements in parallel simulation and successful robotic applications are spurring a resurgence in sampling-based model predictive control. To build on this progress, howe…
Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
Jan Brüdigam, Ali-Adeeb Abbas, Maks Sorokin +7
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 re…