Motion planning is a fundamental problem in autonomous robotics that requires finding a
path to a specified goal that avoids obstacles and takes into account a robot's limitations and …

Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of
robustness and agility over a variety of terrains. However, that work, and most prior bipedal …

To efficiently achieve complex humanoid loco-manipulation tasks in industrial contexts, we
propose a combined vision-based tracker-localization interplay integrated as part of a task …

In this letter, we propose an efficient and highly versatile loco-manipulation planning for
humanoid robots. Loco-manipulation planning is a key technological brick enabling …

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation
learning. The difficulty of collecting task demonstrations and training policies for humanoids …

In this work we propose a learning-based approach to box loco-manipulation for a
humanoid robot. This is a particularly challenging problem due to the need for whole-body …

We present a method that finds locomanipulation plans that perform simultaneous
locomotion and manipulation of objects for a desired end-effector trajectory. Key to our …

Search-based planning for relatively low-dimensional motion-planning problems such as for
autonomous navigation and autonomous flight has been shown to be very successful. Such …

Wheeled-legged robots have the potential to navigate in cluttered and irregular scenarios by
altering the locomotion modes to adapt to the terrain challenges and effectively reach …

Despite impressive advances of humanoid robotics, the autonomous planning of whole-
body loco-manipulation actions in unknown environments is still an open problem. In our …