Numerical Optimization for Online Multi-Contact Motion Planning and Control

Organizers: Romeo Orsolino, Carlos Mastalli, Michele Focchi, Nicolas Mansard


What if legged robots were able to generate dynamic motions in real-time while interacting with a complex environment? Such technology would represent a significant step forward the deployment of legged systems in real world scenarios. This means being able to replace humans in the execution of dangerous tasks and to collaborate with them in industrial applications.

Numerical optimization and data-driven algorithms can help us tackle this challenge and enable motion planning and control for legged robotic systems in complex geometry environments (e.g. multi-contact scenarios). Indeed, when the complexity of the terrain increases, or when the execution of the requested task involves highly dynamic motions, numerical optimization and machine learning strategies are needed to automatically find feasible trajectories and control actions that could not otherwise be determined.

The presence of obstacles, possible disturbances and/or modeling errors makes it necessary to find those control policies in the order of milliseconds so that the robot can immediately compensate unexpected events and re-plan suitable reactions.

This workshop aims to bring together researchers from all the relevant communities in legged locomotion such as: numerical optimization, machine learning (ML), model predictive control (MPC) and computational geometry in order to chart the most promising methods to address the above-mentioned scientific challenges.