Robot Learning Assembly Tasks from Human Demonstrations

The industry robots are widely deployed in the assembly and production lines as they are efficient in performing highly repetitive tasks. They are mainly position-controlled and pre-programmed to work in well-structured environments. However, they cannot deal with dynamical changes and unexpected events in their operations as they do not have sufficient sensing and learning capabilities. It remains a big challenge for robotic assembly operations to be conducted in unstructured environments today. This thesis research focuses on the development of robot learning from demonstration (LfD) for the robotic assembly task by using visual teaching. Firstly, the human kinesthetic teaching method is adopted for robot to learn an effective grasping skill in unstructured environment. During this teaching process, the robot learns the object's SIFT feature and grasping pose from human demonstrations. Secondly, a novel skeleton-joint mapping framework is proposed for robot learning from human demonstrations. The mapping algorithm transfers the human motion from the human joint space to the robot motor space so that the robot can be taught intuitively in a remote place. Thirdly, a novel visual-mapping demonstration framework is built for robot learning assembly tasks, in which, the demonstrator is able to teach the robot with feedback in real-time. Gaussian Mixture Model and Gaussian Mixture Regression are used to encode the learned skills for the robot. Finally, The effectiveness of the approach is evaluated with practical assembly tasks by the Baxter robot. The significance of this thesis research is on its comprehensive insight of robot learning from demonstration for assembly tasks. The proposed LfD paradigm has the potential to effectively transfer human skills to robots both in industrial and domestic environments. It paves the way for general public to use the robots without the need of programming skills

Autonomous Topological Optimisation for Multi-robot Systems in Logistics

Multi-robot systems (MRS) are currently being introduced in many in-field logistics operations in large environments such as warehouses and commercial soft-fruit production. Collision avoidance is a critical problem in MRS as it may introduce deadlocks during the motion planning. In this work, a discretised topological map representation is used for low-cost route planning of individual robots as well as to easily switch the navigation actions depending on the constraints in the environment. However, this topological map could also have bottlenecks which leads to deadlocks and low transportation efficiency when used for an MRS. In this paper, we propose a resource container based Request-Release-Interrupt (RRI) algorithm that constrains each topological node with a capacity of one entity and therefore helps to avoid collisions and detect deadlocks. Furthermore, we integrate a Genetic Algorithm (GA) with Discrete Event Simulation (DES) for optimising the topological map to reduce deadlocks and improve transportation efficiency in logistics tasks. Performance analysis of the proposed algorithms are conducted after running a set of simulations with multiple robots and different maps. The results validate the effectiveness of our algorithms

Robot Performing Peg-in-Hole Operations by Learning from Human Demonstration

This paper presents a novel approach for a robot to conduct assembly tasks, namely robot learning from human demonstrations. The learning of robotic assembly task is divided into two phases: teaching and reproduction. During the teaching phase, a wrist camera is used to scan the object on the workbench and extract its SIFT feature. The human demonstrator teaches the robot to grasp the object from the effective position and orientation. During the reproduction phase, the robot uses the learned knowledge to reproduce the grasping manipulation autonomously. The robustness of the robotic assembly system is evaluated through a series of grasping trials. The dual-arm Baxter robot is used to perform the Peg-in-Hole task by using the proposed approach. Experimental results show that the robot is able to accomplish assembly task by learning from human demonstration without traditional dedicated programming

Maximising availability of transportation robots through intelligent allocation of parking spaces

Autonomous agricultural robots increasingly have an important role in tasks such as transportation, crop monitoring, weed detection etc. These tasks require the robots to travel to different locations in the field. Reducing time for this travel can greatly reduce the global task completion time and improve the availability of the robot to perform more number of tasks. Looking at in-field logistics robots for supporting human fruit pickers as a relevant scenario, this research deals with the design of various algorithms for automated allocation of parking spaces for the on-field robots, so as to make them most accessible to preferred areas of the field. These parking space allocation algorithms are tested for their performance by varying initial parameters like the size of the field, number of farm workers in the field, position of the farm workers etc. Various experiments are conducted for this purpose on a simulated environment. Their results are studied and discussed for better understanding about the contribution of intelligent parking space allocation towards improving the overall time efficiency of task completion

Enhanced robotic hand-eye coordination inspired from human-like behavioral patterns

Robotic hand-eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand-eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method: Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse channel, a human-like behavior pattern: “Stop-to-Fixate”, is imparted to the robot, thereby giving the robot an enhanced reaching ability. A visual processing system inspired by the human retina structure is used to compress visual information so as to reduce the robot’s learning complexity. In addition, two constructive neural networks establish the two sensory delivery channels. The experimental results demonstrate that the robotic system gradually obtains a reaching ability. In particular, when the robotic hand touches an unseen object, the reverse channel successfully drives the visual system to notice the unseen object

A Developmental Learning Approach of Mobile Manipulator via Playing

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games

A Reduced Classifier Ensemble Approach to Human Gesture Classification for Robotic Chinese Handwriting

The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character’s strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble’s size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke’s trajectory to the robot’s motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters

The R Protein of SARS-CoV: Analyses of Structure and Function Based on Four Complete Genome Sequences of Isolates BJ01-BJ04

The R (replicase) protein is the uniquely defined non-structural protein (NSP) responsible for RNA replication, mutation rate or fidelity, regulation of transcription in coronaviruses and many other ssRNA viruses. Based on our complete genome sequences of four isolates (BJ01-BJ04) of SARS-CoV from Beijing, China, we analyzed the structure and predicted functions of the R protein in comparison with 13 other isolates of SARS-CoV and 6 other coronaviruses. The entire ORF (open-reading frame) encodes for two major enzyme activities, RNA-dependent RNA polymerase (RdRp) and proteinase activities. The R polyprotein undergoes a complex proteolytic process to produce 15 function-related peptides. A hydrophobic domain (HOD) and a hydrophilic domain (HID) are newly identified within NSP1. The substitution rate of the R protein is close to the average of the SARS-CoV genome. The functional domains in all NSPs of the R protein give different phylogenetic results that suggest their different mutation rate under selective pressure. Eleven highly conserved regions in RdRp and twelve cleavage sites by 3CLP (chymotrypsin-like protein) have been identified as potential drug targets. Findings suggest that it is possible to obtain information about the phylogeny of SARS-CoV, as well as potential tools for drug design, genotyping and diagnostics of SARS

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead