  The deployment of robots within realistic environments requires the
capability to plan and refine the loco-manipulation trajectories on the fly to
avoid unexpected interactions with a dynamic environment. This extended
abstract provides a pipeline to offline plan a configuration space global
trajectory based on a randomized strategy, and to online locally refine it
depending on any change of the dynamic environment and the robot state. The
offline planner directly plans in the contact space, and additionally seeks for
whole-body feasible configurations compliant with the sampled contact states.
The planned trajectory, made by a discrete set of contacts and configurations,
can be seen as a graph and it can be online refined during the execution of the
global trajectory. The online refinement is carried out by a graph optimization
planner exploiting visual information. It locally acts on the global initial
plan to account for possible changes in the environment. While the offline
planner is a concluded work, tested on the humanoid COMAN+, the online local
planner is still a work-in-progress which has been tested on a reduced model of
the CENTAURO robot to avoid dynamic and static obstacles interfering with a
wheeled motion task. Both the COMAN+ and the CENTAURO robots have been designed
at the Italian Institute of Technology (IIT).
