SOURCE-LINKED INTELLIGENCE

LiftMeUp: Globally optimal algorithms for dexterous manipulation and locomotion

CORDIS · observation · Publication date unknown

le and resource-hungry deep-learning solutions for robotics. Furthermore, LiftMeUp builds on providing certifiably optimal methods with important consequences for safety and efficiency, as opposed to deep learning and local solvers, where different initializations can lead to entirely different solutions. LiftMeUp is carried out at WILLOW, Inria Paris, known for cutting-edge control and locomotion research, and has three stages: first, combining concepts from Koopman theory, polynomial optimization, and kernel methods, lifting functions are inferred from data and integrated into globally optimal methods for state estimation and control. Second, different models are optimally combined, leading to a modular framework that can be incrementally updated online. Lastly, these novel algorithms are implemented on hardware to solve real-world locomotion and dexterous manipulation tasks. This framework will have an important scientific impact by creating novel connections between global optimization and machine learning, enabling the use of principled over heuristic solvers in a broad range

Read original source ↗ Open in workspace

recordType

award

status

TERMINATED

region

EU

value

226420.56

unit

EUR