Artificial Intelligence and Machine Learning | Report

Artificial Intelligence and Machine Learning
Introduction:
The study of humanoid robots includes various fields like artificial intelligence (Wortham, et.
al. , 2016), development of hardware (Song & Lee, 2015) as well as the interaction of the
humans and robots (Chen,et. al. , 2012). In the twenty first century, there is a need to develop
robots which are human – friendly because the future robots need to interact with the human
beings living in a society. Robots were used in industries for the assembly of various parts
and other activities (Kaljun & Dolšak, 2012). But now, the advanced form of robots must be
able to speak, walk as well as recognise others (Gretzel, 2011). Humanoid robots have many
similarities to the human beings (Clerc,2014). However, humanoid robots are very expensive,
complicated and unstable in nature (Li, et. al. , 2011). Here, the control system is being
considered for the motion (real – time) on the basis of feedback provided from sensory input.
The actuators are needed for torque, power and speed purpose. The range of movable joint
angle must be good to make the humanoid resemble a human being more.
The fetch – carry robot can be developed for helping the people who are physically impaired
while working in an office (Wortham, et. al., 2016 ),. As per Widodo, Shim, et. al. ,2011), the
robot’s platform can be enhanced by using a natural language interface or the graphical
interface. The ISR (Intelligent Service Robot) can be designed for helping the users, helping
fetch and deliver objects in the office environment (Anastasi, Corucci & Marcelloni, 2011).
For developing proper social interaction for robots and people, some degrees of
anthropomorphism can be employed in the physical design as well as behaviour of the robot
(Song & Lee, 2015). If the robot enters a social space, then we unknowingly present our
interpretation over their actions (Chen, et. al., 2011). This intensity of anthropomorphising
gives an opportunity to develop the social robots (Shih, et. al. ,2011). The robots need to
become a part of the social environment as well as the physical (Gretzel, 2011).
Scaramuzza, Achtelik, Doitsidis, Friedrich, Kosmatopoulos, Martinelli & Gurdan (2014)
have shown that the overall social understanding can be improved by adding the human like

features to the robot so that it can interact with the people socially. There must be a head,
eyes as well as mouth which include the anthropomorphic characteristics (Li, Gu, Chen &
Zhu, 2011). The robot must be able to complete its self-goals as well as the community goals
(Li, Zhou, Yang, & Zheng, 2016). In order to use anthropomorphism in the robot system, a
detailed study of the human – robot interaction needs to be done.
All the human qualities must not be added as it may pose a problem like the robot becoming
selfish (Kitano, Kobayashi, Sakakibara, Kawano, Sakai, Uematsu & Murai, 2012). The major
aim must be to reduce the gap between human beings and the machines which use the digital
information. The social robots are expected to show Artificial Intelligence (AI) features.
There are 2 aspects of AI – Strong AI and Weak AI (Khooban, 2014). The strong AI can
make it feasible to produce an artificial system which duplicates the human intelligence. The
weak AI means that we can only simulate aspects of human intelligence (Lin & Li, 2013).
Earlier the robots were used in the field of production lines for carrying out the assembly
operations (McDuff, Karlson, Kapoor, Roseway & Czerwinski, 2012). But now they have to
be present in a social environment where they are expected to respond and adapt to the moods
of people (Chen, Shih & Chou, 2012). The concept of ‘emotional intelligence’ has to be
developed here (Ongenae, Duysburgh, Verstraete, Sulmon, Bleumers, Jacobs & De, 2012). It
can measure various parameters and respond accordingly. According to Clerc (2014), the
humans are trying to build a robot which is most similar to themselves.
Aims / Objectives:
The goal is to develop an intelligent humanoid robotic system. It must be reliable and able to
implement dynamic walking and must support interaction with robots and human beings. It
must include the concept of Artificial Intelligence (AI), be able to recognise images and
implement proper navigation. The main focus lies on dynamic walking. The various
mechanical components use simple structures which are easy to machine using a 2 – D
process of simulation for walking.

Design of System:
The humanoid will have a total of 41 degrees of freedom (DOFs). A distributed type of
control architecture will be used for controlling all the joint axes properly. This provides an
efficient system with a decreased burden of computation on the main controller.
The basic points which have been kept in mind while designing the system are : the shape as
well as the movements must be like human, lower weight, small size and no backlash in
actuator, a simple kinematic design, lesser power consumed and a system which is self –
contained ( De-An, Jidong , Wei , Ying & Yu , 2011 ).
The DC motor as well as harmonic drive gears are used as they can be controlled easily and
also reduce the backlash. The various controllers, sensory devices as well as the batteries are
placed inside the system. A wireless LAN is used to carry out various operations using a
notebook PC. There needs to be optimization of the structure mechanically to decide the
shape as well as thickness (Kaljun & Dolšak, 2012). The design is such that the axes of all the
major joints meet at a single point. This gives a simple closed – form solution for the inverse
kinematics.
The Machine Learning can be employed for teaching a robot how to walk by training the
models. Deep learning is a type of machine learning model which can teach the robot to learn
by itself in a repeating manner. A large amount of quality data is needed for the same. The
experience gained while performing various experiments and in various regions like rugged
terrain, mud area, rough and uneven area. The supervised learning is used here. A 2-leg robot
needs more balancing factor than 4 leg robots. The legs must be trained move as left and right
in an fashion to proceed forward and backward when required. The data is given to the model
and then reinforcement learning is applied. The training of the neural networks is done on the
basis of weight and then checking of the movements is done based on the same. The result
needs to be maximized for efficiency. A positive reward is assigned in case of a correct
outcome and a negative reward is assigned in case of a wrong outcome obtained from the
image gyroscope and the network is then re – trained. This way, the robot’s leg is capable to
teaching itself in a very small time.
The movements can be controlled by the help of an Artificial Neural Network , ANN for
helping the robot. The reinforcement learning using the ANN as well as motor babbling are
combined, random movements are attempted by the system. The system learns the properties
by using a movement’s results obtained using the image gyroscope. A reward is given when a