Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization, and quantitative result

Development of a Locomotion and Balancing Strategy for Humanoid Robots

The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage, makes the gait unnatural, energy inefficient and exert large amounts of torque to the knee joint. Thus creating a walking engine that produces a quality and natural gait is essential for humanoid robots in general and is a factor for succeeding in RoboCup competition. Humanoids robots are required to walk fast to be practical for various life tasks. However, its complex structure makes it prone to falling during fast locomotion. On the same hand, the robots are expected to work in constantly changing environments alongside humans and robots, which increase the chance of collisions. Several human-inspired recovery strategies have been studied and adopted to humanoid robots in order to face unexpected and avoidable perturbations. These strategies include hip, ankle, and stepping, however, the use of the arms as a recovery strategy did not enjoy as much attention. The arms can be employed in different motions for fall prevention. The arm rotation strategy can be employed to control the angular momentum of the body and help to regain balance. In this master\u27s thesis, I developed a detailed study of different ways in which the arms can be used to enhance the balance recovery of the NAO humanoid robot while stationary and during locomotion. I model the robot as a linear inverted pendulum plus a flywheel to account for the angular momentum change at the CoM. I considered the role of the arms in changing the body\u27s moment of inertia which help to prevent the robot from falling or to decrease the falling impact. I propose a control algorithm that integrates the arm rotation strategy with the on-board sensors of the NAO. Additionally, I present a simple method to control the amount of recovery from rotating the arms. I also discuss the limitation of the strategy and how it can have a negative impact if it was misused. I present simulations to evaluate the approach in keeping the robot stable against various disturbance sources. The results show the success of the approach in keeping the NAO stable against various perturbations. Finally,I adopt the arm rotation to stabilize the ball kick, which is a common reason for falling in the soccer humanoid RoboCup competitions

Visual Imitation Learning with Recurrent Siamese Networks

It would be desirable for a reinforcement learning (RL) based agent to learn behaviour by merely watching a demonstration. However, defining rewards that facilitate this goal within the RL paradigm remains a challenge. Here we address this problem with Siamese networks, trained to compute distances between observed behaviours and the agent's behaviours. Given a desired motion such Siamese networks can be used to provide a reward signal to an RL agent via the distance between the desired motion and the agent's motion. We experiment with an RNN-based comparator model that can compute distances in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we have had also found that the inclusion of multi-task data and an additional image encoding loss helps enforce the temporal consistency. These two components appear to balance reward for matching a specific instance of behaviour versus that behaviour in general. Furthermore, we focus here on a particularly challenging form of this problem where only a single demonstration is provided for a given task -- the one-shot learning setting. We demonstrate our approach on humanoid agents in both 2D with $10$ degrees of freedom (DoF) and 3D with $38$ DoF.Comment: PrePrin

Modeling humanoid swarm robots with petri nets

Master's thesis in Computer scienceRobots have become a hot topic in today‟s electronic world. There are many definitions for it. One of the definition in Oxford dictionary states “a robot is a machine capable for carrying out a complex series of action automatically especially one programmable by a computer”. This paper deals with a special kind of robot, which is also known as humanoid robot. These robots are replication of human beings with head, torso, arms and legs. A model of human is presented in this paper as discrete event system adapted from “Modeling and simulating motions of human bodies…”[1]. This model consists of sixteen interrelated limbs defined in 3D space, so most limbs/joints are able to make movement in three different angles (α, β and γ). Full details regarding Range of Motion (ROM) of rigid body in forward kinematic is illustrated. Human motions are categorized into two types: stochastic and deterministic motions. Deterministic motions are demonstrated using gait cycle of walking and running of normal adult person. The main focus of this paper is to model and simulate humanoid robot represented as Discrete Event Systems (DES); in Petri Net using GPenSIM and later expand those group of robots to swarm setting. GPenSIM is General Purpose Petri Net simulator [2] developed as toolbox for MATLAB to model and simulated discrete events using Petri net tools. Each joint‟s angle is treated as a separate Petri Net model which is independent from each other and their movement‟s limits are defined by ROM of normal human body. The instructions relating to the motion of joints for simulation are fed through a file to the instructor. These movements of joints are represented by variation of tokens displayed at the end of simulation in a graphical figure. Further, same structure of model is used in swarm of robots. Instead of feeding instructions to individual robots, a central instructor is created. This instructor acts as a master to robots acting as slaves where slaves include some predetermined commands embedded inside them. With central command system, a proper synchronization is achieved among group of robots working as swarm. A normal routine of group dance or simple group sport can be accomplished with calculated instructions on this swarm of robot