Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

The JPL telerobotic Manipulator Control and Mechanization (MCM) subsystem

The Manipulator Control and Mechanization (MCM) subsystem of the telerobot system provides the real-time control of the robot manipulators in autonomous and teleoperated modes and real time input/output for a variety of sensors and actuators. Substantial hardware and software are included in this subsystem which interfaces in the hierarchy of the telerobot system with the other subsystems. The other subsystems are: run time control, task planning and reasoning, sensing and perception, and operator control subsystem. The architecture of the MCM subsystem, its capabilities, and details of various hardware and software elements are described. Important improvements in the MCM subsystem over the first version are: dual arm coordinated trajectory generation and control, addition of integrated teleoperation, shared control capability, replacement of the ultimate controllers with motor controllers, and substantial increase in real time processing capability

NASA/NBS (National Aeronautics and Space Administration/National Bureau of Standards) standard reference model for telerobot control system architecture (NASREM)

The document describes the NASA Standard Reference Model (NASREM) Architecture for the Space Station Telerobot Control System. It defines the functional requirements and high level specifications of the control system for the NASA space Station document for the functional specification, and a guideline for the development of the control system architecture, of the 10C Flight Telerobot Servicer. The NASREM telerobot control system architecture defines a set of standard modules and interfaces which facilitates software design, development, validation, and test, and make possible the integration of telerobotics software from a wide variety of sources. Standard interfaces also provide the software hooks necessary to incrementally upgrade future Flight Telerobot Systems as new capabilities develop in computer science, robotics, and autonomous system control

Coordination of several robots based on temporal synchronization

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper proposes an approach to deal with the problem of coordinating multi-robot systems, in which each robot executes individually planned tasks in a shared workspace. The approach is a decoupled method that can coordinate the participating robots in on-line mode. The coordination is achieved through the adjustment of the time evolution of each robot along its original planned geometric path according to the movements of the other robots to assure a collision-free execution of their respective tasks. To assess the proposed approach different tests were performed in graphical simulations and real experiments.Postprint (published version

From High-Level Task Descriptions to Executable Robot Code

For robots to be productive co-workers in the manufacturing industry, it is necessary that their human colleagues can interact with them and instruct them in a simple manner. The goal of our research is to lower the threshold for humans to instruct manipulation tasks, especially sensorcontrolled assembly. In our previous work we have presented tools for high-level task instruction, while in this paper we present how these symbolic descriptions of object manipulation are translated into executable code for our hybrid industrial robot controllers

Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations

In robotics, there is a need of an interactive and expedite learning method as experience is expensive. In this research, we propose two different methods to make a humanoid robot learn manipulation tasks: Learning by trial-and-error, and Learning from demonstrations. Just like the way a child learns a new task assigned to him by trying all possible alternatives and further learning from his mistakes, the robot learns in the same manner in learning by trial-and error. We used Q-learning algorithm, in which the robot tries all the possible ways to do a task and creates a matrix that consists of Q-values based on the rewards it received for the actions performed. Using this method, the robot was made to learn dance moves based on a music track. Robot Learning from Demonstrations (RLfD) enable a human user to add new capabilities to a robot in an intuitive manner without explicitly reprogramming it. In this method, the robot learns skill from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated task or trajectory using Hidden Markov Model (HMM). The learned model is further used to produce a generalized trajectory. In the end, we discuss the differences between two developed systems and make conclusions based on the experiments performed

Learning and Composing Primitive Skills for Dual-Arm Manipulation

In an attempt to confer robots with complex manipulation capabilities, dual-arm anthropomorphic systems have become an important research topic in the robotics community. Most approaches in the literature rely upon a great understanding of the dynamics underlying the system's behaviour and yet offer limited autonomous generalisation capabilities. To address these limitations, this work proposes a modelisation for dual-arm manipulators based on dynamic movement primitives laying in two orthogonal spaces. The modularity and learning capabilities of this model are leveraged to formulate a novel end-to-end learning-based framework which (i) learns a library of primitive skills from human demonstrations, and (ii) composes such knowledge simultaneously and sequentially to confront novel scenarios. The feasibility of the proposal is evaluated by teaching the iCub humanoid the basic skills to succeed on simulated dual-arm pick-and-place tasks. The results suggest the learning and generalisation capabilities of the proposed framework extend to autonomously conduct undemonstrated dual-arm manipulation tasks.Comment: Annual Conference Towards Autonomous Robotic Systems (TAROS19

Interactive Imitation Learning of Bimanual Movement Primitives

Performing bimanual tasks with dual robotic setups can drastically increase the impact on industrial and daily life applications. However, performing a bimanual task brings many challenges, like synchronization and coordination of the single-arm policies. This article proposes the Safe, Interactive Movement Primitives Learning (SIMPLe) algorithm, to teach and correct single or dual arm impedance policies directly from human kinesthetic demonstrations. Moreover, it proposes a novel graph encoding of the policy based on Gaussian Process Regression (GPR) where the single-arm motion is guaranteed to converge close to the trajectory and then towards the demonstrated goal. Regulation of the robot stiffness according to the epistemic uncertainty of the policy allows for easily reshaping the motion with human feedback and/or adapting to external perturbations. We tested the SIMPLe algorithm on a real dual-arm setup where the teacher gave separate single-arm demonstrations and then successfully synchronized them only using kinesthetic feedback or where the original bimanual demonstration was locally reshaped to pick a box at a different height