  Humanoid robots could benefit from using their upper bodies for support
contacts, enhancing their workspace, stability, and ability to perform
contact-rich and pushing tasks. In this paper, we propose a unified approach
that combines an optimization-based multi-contact whole-body controller with
Flow Matching, a recently introduced method capable of generating multi-modal
trajectory distributions for imitation learning. In simulation, we show that
Flow Matching is more appropriate for robotics than Diffusion and traditional
behavior cloning. On a real full-size humanoid robot (Talos), we demonstrate
that our approach can learn a whole-body non-prehensile box-pushing task and
that the robot can close dishwasher drawers by adding contacts with its free
hand when needed for balance. We also introduce a shared autonomy mode for
assisted teleoperation, providing automatic contact placement for tasks not
covered in the demonstrations. Full experimental videos are available at:
https://hucebot.github.io/flow_multisupport_website/
