Humanoid Robotic Language and Virtual Reality Simulation
7
4.4 Automatic Constraint Satisfaction
We proposed to use Automatic Constraint Satisfaction to reduce the complexity of
specifying humanoid motions. There are many implicit requirements for locomotion, such
as maintaining balance and structural integrity. Automatic constraint satisfaction system
will provide additional changes to meet the implicit requirements.
A system for providing automatic constraint satisfaction for locomotion is very complex and
much research is being done on areas such as motion planning and constraint satisfaction.
For example, we can simply specify that the robot must move its right hand from current
position (3, 6, 2, 0) to new position (3, 50, 2, 6). The simpler the specification, in most cases,
requires the more complex constraint satisfaction. In our example, the hand must reach the
new position using 6 units of time, so that speeds for various actuators must be adjusted to
meet this requirement. If the hand cannot reach the new position by simply raising it and
reaching out, then the robot must move the whole body toward the new position.
5. Acquiring New Motor Skills
The ability for acquiring new motor skills is essential for mental developments. The trivial
approach is to simply program a humanoid robot for new required motor skills (Ude et al.,
2004), which can easily be done by an experienced programmer using our proposed
language and framework. Thus, in the following we will focus on strategies for acquiring
motor skills through learning from trial and error and learning by macro approach.
Learning motor skills has not yet been a central focus of Machine Learning researchers.
Thus, much research remains to be done on automatic acquiring new motor skills. We
briefly outline strategies for creating such a system, which in part is based on the author’s
work on automata for learning sequential tasks (Choi, 1998; 2003).
5.1 Learning from Trial and Error
One way for acquiring new motor skills is by trial and error. This approach requires first
identifying an objective and then selecting actions or motions to achieve the objective. In
particularly, using our proposed framework, identifying an objective can be described as
identifying a goal state, while selecting actions or motions can be described as selecting a
sequence of transitions from the current state to the goal state.
Using our proposed framework, the underlining model is a non-deterministic finite state
machine (Choi 2002). From one state, there may be several transitions to follow to other
states. Choosing one transitions or the other is why we call this a trial and error approach
and is why it is non-deterministic. To achieve the objective is to find a sequence of
transitions from the current state to the goal state. As soon as a sequence of transitions is
found, it can be stored for future use.
5.2 Learning by Macro Approach
Macro approach can be used in a supervised learning environment (Bentivegna & Atkeson,
2001; Arsenic, 2004; Calinon et al. 2007). After a humanoid robot is repeatedly instructed to
perform certain task, the robot can store the sequence of motions to associate with a name of
the task. After which, the robot can be commanded to perform the sequence by simply
specifying the name of the task. This is a simple record and play back approach.
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