It is inevitable that the robots will move from the structured and controlled
environments, such as industry and laboratories, into our daily lives. To achieve
the integration of robots into our daily lives, we have to solve two fundamental
problems related to these tasks. (1) Each individual robot user has a different
environment, needs, tasks and tools. This demands acquisition of a vast amount
of very specific skills, thus the transfer of the skill from the user to the robot must
be intuitive, adaptable and fast. (2) Many daily tasks require us to deal with
physical interaction with unpredictable and unstructured environment. Because
of this, the acquisition of models is very complex and makes the classical robot
control difficult. To make robots successfully operate in such environment, it
is necessary to find alternative ways to teach the robot how do deal with such
interactions. In the first part of the first chapter, there is an introduction into the research
field with an overview of the state-of-the-art. The second part explains the goals
of the thesis. The last part contains an overview of the performed experiments.
The second chapter presents the human-in-the-loop teaching method. The
method is based on human sensorimotor learning ability that allows the demonstrator
to first obtain the skill necessary for controlling the robot and then transfer
that skill to the robot. This is followed by a presentation of methods to encode
the control strategy (skill). These methods include: sensorimotor pairs, trajectory
of motion and adaptive oscillators for describing the state of periodic motion.
In the last part of this chapter, we present two machine learning methods that
were utilised in our methods (Gaussian Process Regression and Locally Weighted
Regression). The third chapter presents the proposed method for teaching humanoid robots
how to deal with physical interaction of its body with human and environment.
To this end, a method for converting robot sensory information into a human
sensory stimulation was developed to give the demonstrator the necessary feedback
about the robot body dynamics during the teaching process. In this scope,
we developed a special haptic interface that exerted forces on the demonstrator's
body. After that, we present a method for on-line robot learning where the
control of the robot body is shared between the currently learnt strategy and human
demonstrator. This enables a gradual transfer of the control responsibility
from the demonstrator to the robot and offers an additional feedback about the
state of the learning. In the last part, we propose a method that allows merging
human-demonstrated posture-control skill with arm motion control based on
inverse kinematics solution. The fourth chapter presents the proposed methods for teaching robot how to manipulate with unstructured and unpredictable environment. These methods
were based on the ability of demonstrator to modulate and teach the impedance
of robotic arm. We developed methods that allow the demonstrator to control
the robot's stiffness in real-time. This approach was then used to solve tasks
related to use of elementary tools, human-robot cooperation and part assembly.
These tasks are crucial for future robot operation in human daily lives or in their
participation in space exploration, where the available means are limited.
The fifth chapter presents a method for exoskeleton control. These devices are
made to enclose the human body parts and directly assist the motion in the joints.
In the framework of integration of robots into the human daily lives, exoskeletons
are a complement to the humanoid robots that are designed to provide assistance
on a more indirect level. The proposed control method is based on minimisation of
human muscle activity through adaptive learning of robot assistive joint torques.
The main advantage of this method is that it does not require models of human
and robot. Necessary compensation torques are adaptively derived according to
the current conditions. The method was validated on multiple subjects and we
analysed the human-robot co-adaptation. The last chapter recapitulates the main contributions of the dissertation and presents its conclusions.