Titolo della tesi: Learning-based methods for planning and control of humanoid robots
Nowadays, humans and robots are more and more likely to coexist as time goes by.
The anthropomorphic nature of humanoid robots facilitates physical human-robot
interaction, and makes social human-robot interaction more natural. Moreover,
it makes humanoids ideal candidates for many applications related to tasks and
environments designed for humans.
No matter the application, an ubiquitous requirement for the humanoid that
cannot be ignored is to possess proper locomotion skills. Despite long-lasting research
in humanoid locomotion, this problem is still far from being a trivial task. A common
approach to humanoid locomotion consists in decomposing the complexity of the
problem by means of a model-based hierarchical control architecture. To cope with
computational constraints, simplified models for the humanoid are employed in some
of the architectural layers. At the same time, the interactive nature of the humanoid
locomotion task as well as its closeness to human locomotion suggest a data-driven
approach to learn such a complex task directly from experience.
This thesis investigates the application of learning-based techniques to planning
and control of humanoid locomotion. In particular, both deep reinforcement learning
and deep supervised learning are considered to address humanoid locomotion tasks
in a crescendo of complexity.
First, we employ deep reinforcement learning to study the spontaneous emergence
of balancing and push recovery strategies for the humanoid, which represent essential
prerequisites for more complex locomotion tasks.
Then, by making use of motion capture data collected from human subjects, we
employ deep supervised learning to shape the robot walking trajectories towards an
improved human-likeness.
The proposed approaches are validated on real and simulated humanoid robots.
Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3.