Framework of active robot learning

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by researchIn recent years, cognitive robots have become an attractive research area of Artificial Intelligent (AI). High-order beliefs for cognitive robots regard the robots' thought about their users' intention and preference. The existing approaches to the development of such beliefs through machine learning rely on particular social cues or specifically defined award functions . Therefore, their applications can be limited. This study carried out primary research on active robot learning (ARL) which facilitates a robot to develop high-order beliefs by actively collecting/discovering evidence it needs. The emphasis is on active learning, but not teaching. Hence, social cues and award functions are not necessary. In this study, the framework of ARL was developed. Fuzzy logic was employed in the framework for controlling robot and for identifying high-order beliefs. A simulation environment was set up where a human and a cognitive robot were modelled using MATLAB, and ARL was implemented through simulation. Simulations were also performed in this study where the human and the robot tried to jointly lift a stick and keep the stick level. The simulation results show that under the framework a robot is able to discover the evidence it needs to confirm its user's intention

Fuzzy optimisation based symbolic grounding for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySymbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms

Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot

Recently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. SEMG (Surface Electromyography) of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. The absolute variance of 3-layer wavelet coefficients is distilled and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation and forearm rotation. Experiments verify the effectiveness of the proposed method

Active robot learning for building up high-order beliefs

High-order beliefs of service robots regard the robots' thought about their users' intention and preference. The existing approaches to the development of such beliefs through machine learning rely on particular social cues or specifically defined award functions. Their applications can, therefore, be limited. This paper presents an active robot learning approach to facilitate the robots to develop the beliefs by actively collecting/discovering evidence they need. The emphasis is on active learning. Hence social cues and award functions are not necessary. Simulations show that the presented approach successfully enabled a robot to discover evidences it needs

Fuzzy optimisation based symbolic grounding for service robots

Symbolic grounding is a bridge between high-level planning and actual robot sensing, and actuation. Uncertainties raised by the unstructured environment make a bottleneck for integrating traditional artificial intelligence with service robotics. This paper presents a fuzzy logic based approach to formalise the grounding problems into a fuzzy optimization problem, which is robust to uncertainties. Novel techniques are applied to establish the objective function, to model fuzzy constraints and to perform fuzzy optimisation. The outcome is tested with a service robot fetch and carry task, where the fuzzy optimisation approach helps the robot to determine the most comfortable position (location and orientation) for grasping objects. Experimental results show that the proposed approach improves the robustness of the task implementation in unstructured environments

Fuzzy logic based symbolic grounding for best grasp pose for homecare robotics

Symbolic grounding in unstructured environments remains an important challenge in robotics [7]. Homecare robots are often required to be instructed by their human users intuitively, which means the robots are expected to take highlevel commands and execute corresponding tasks in a domestic environment. High-level commands are represented with symbolic terms such as “near” and “close” and, on the other hand, robots are controlled based on trajectories. The robots need to translate the symbolic terms to trajectories. In addition, domestic environment is unstructured where the same objects can be placed in different places over the time. This increases the difficulties in symbolic grounding. This paper presents a fuzzy logic based approach to symbolic grounding. In this approach, grounded concepts are modelled as fuzzy sets and the existing knowledge is used to deduce grounded values given real-time sensory inputs. Experiments results show that this approach works well in unstructured environment

Integration of symbolic task planning into operations within an unstructured environment

To ensure a robot capable of robust task execution in unstructured environments, task planners need to have a high-level understanding of the nature of the world, reasoning for deliberate actions, and reacting to environment changes. Proposed is a practical task planning approach that seamlessly integrating deeper domain knowledge, real time perception and symbolic planning for robot operation. A higher degree of autonomy under unstructured environment will be endowed to the robot with the proposed approach