Information-theoretic measures as a generic approach to human-robot interaction : Application in CORBYS project

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/AuthorThe objective of the CORBYS project is to design and implement a robot control architecture that allows the integration of high-level cognitive control modules, such as a semantically-driven self-awareness module and a cognitive framework for anticipation of, and synergy with, human behaviour based on biologically-inspired information-theoretic principles. CORBYS aims to provide a generic control architecture to benefit a wide range of applications where robots work in synergy with humans, ranging from mobile robots such as robotic followers to gait rehabilitation robots. The behaviour of the two demonstrators, used for validating this architecture, will each be driven by a combination of task specific algorithms and generic cognitive algorithms. In this paper we focus on the generic algorithms based on information theoryFinal Accepted Versio

Analysis and Design of Standard Telerobotic Control Software

The Robotics and Automation Center for Excellence (RACE) has defined an open telerobotics control architecture. This architecture, called the Unified Telerobotic Architecture Project (UTAP), is a proposed standard for all Air Force telerobotic systems. Implementation of UTAP will reduce the cost of robotic applications by increasing software modularity, portability, and reusability. This thesis continued the effort to prove the feasibility of UTAP. In December, 1995, 1st Lt Anchor implemented a portion of the UTAP specification on a PUMA robot. The UTAP-compliant controller exhibited some degradation in the system performance. However, the performance degradation was not fully measured. This thesis extended the measurements of Anchor\u27s implementation. Additionally, a portion of the UTAP specification was implemented on an Adept 550 manipulator and the performance effects were measured. The implementation included portions of the generic, robot/axis servo control, tool control, sensor control, programmable 10, subsystem task level control, task description and supervision, parent task program sequencer, task program sequencer, and object knowledge modules. Performance measurements of this implementation indicated that, although performance was adversely affected, the degradation was caused by the interface between the UTAP-compliant application and the non-UTAP-compliant operating system. There was little difference between the complaint and non-compliant applications. Successful implementation of the UTAP specification on the PUMA and Adept manipulators proves that it is a feasible telerobotic architecture. Further study of the specification is recommended. Specifically, the development of a UTAP-compliant operating system should be continued

Design and Evaluation of Standard Telerobotic Control Software

This thesis represents the first implementation of a proposed Air Force standard telerobotic control architecture. This architecture was developed by the NASA Jet Propulsion Laboratory and the National Institute of Standards and Technology under contract to the Air Force Materiel Command Robotics and Automation Center of Excellence (RACE) as the Unified Telerobotics Architecture Project (UTAP). The AFIT Robotics and Automation Applications Group (RAAG) Lab B facility computational structure was redesigned to be compliant with the UTAP architecture. This thesis shows that the UTAP specification to be implementable. However, if the underlying operating system does not support generic message passing, an interface layer must be implemented to access operating system functions. The UTAP compliant controller implemented the robot servo control, object knowledge base, and user interface components of the specification. The controller performed adequately although there was degradation in the performance as evidenced by increased error during trajectories. We believe this error can be reduced by re-tuning the controller gains. Further study of the UTAP specification is recommended: additional functions such as external sensor readings should be added; implementation of the specification on different operating systems and robot platforms will prove the transportability of the specification

CORBYS cognitive control architecture for robotic follower

In this paper the novel generic cognitive robot control architecture CORBYS is presented. The objective of the CORBYS architecture is the integration of high-level cognitive modules to support robot functioning in dynamic environments including interacting with humans. This paper presents the preliminary integration of the CORBYS architecture to support a robotic follower. Experimental results on high-level empowerment-based trajectory planning have demonstrated the effectiveness of ROS-based communication between distributed modules developed in a multi-site research environment as typical for distributed collaborative projects such as CORBYS

An Adaptable Approach to Learn Realistic Legged Locomotion without Examples

Learning controllers that reproduce legged locomotion in nature has been a long-time goal in robotics and computer graphics. While yielding promising results, recent approaches are not yet flexible enough to be applicable to legged systems of different morphologies. This is partly because they often rely on precise motion capture references or elaborate learning environments that ensure the naturality of the emergent locomotion gaits but prevent generalization. This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. Leveraging on the exploration capacities of Reinforcement Learning (RL), we learn a control policy that fills in the information gap between the template model and full-body dynamics required to maintain stable and periodic locomotion. The proposed approach can be applied to robots of different sizes and morphologies and adapted to any RL technique and control architecture. We present experimental results showing that even in a model-free setup and with a simple reactive control architecture, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot. And most importantly, this is achieved without using motion capture, strong constraints in the dynamics or kinematics of the robot, nor prescribing limb coordination. We provide supplemental videos for qualitative analysis of the naturality of the learned gaits.Comment: Accepted to ICRA 202

Towards an Architecture for Semiautonomous Robot Telecontrol Systems.

The design and development of a computational system to support robot–operator collaboration is a challenging task, not only because of the overall system complexity, but furthermore because of the involvement of different technical and scientific disciplines, namely, Software Engineering, Psychology and Artificial Intelligence, among others. In our opinion the approach generally used to face this type of project is based on system architectures inherited from the development of autonomous robots and therefore fails to incorporate explicitly the role of the operator, i.e. these architectures lack a view that help the operator to see him/herself as an integral part of the system. The goal of this paper is to provide a human-centered paradigm that makes it possible to create this kind of view of the system architecture. This architectural description includes the definition of the role of operator and autonomous behaviour of the robot, it identifies the shared knowledge, and it helps the operator to see the robot as an intentional being as himself/herself

Multi-bot Easy Control Hierarchy

The goal of our project is to create a software architecture that makes it possible to easily control a multi-robot system, as well as seamlessly change control modes during operation. The different control schemes first include the ability to implement on-board and off-board controllers. Second, the commands can specify either actuator level, vehicle level, or fleet level behavior. Finally, motion can be specified by giving a waypoint and time constraint, a velocity and heading, or a throttle and angle. Our code is abstracted so that any type of robot - ranging from ones that use a differential drive set up, to three-wheeled holonomic platforms, to quadcopters - can be added to the system by simply writing drivers that interface with the hardware used and by implementing math packages that do the required calculations. Our team has successfully demonstrated piloting a single robots while switching between waypoint navigation and a joystick controller. In addition, we have demonstrated the synchronized control of two robots using joystick control. Future work includes implementing a more robust cluster control, including off-board functionality, and incorporating our architecture into different types of robots

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL

FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration

Neural networks are widely used in pattern recognition, security applications and robot control. We propose a hardware architecture system; using Tiny Neural Networks (TNN) specialized in image recognition. The generic TNN architecture allows expandability by means of mapping several Basic units (layers) and dynamic reconfiguration; depending on the application specific demands. One of the most important features of Tiny Neural Networks (TNN) is their learning ability. Weight modification and architecture reconfiguration can be carried out at run time. Our system performs shape identification by the interpretation of their singularities. This is achieved by interconnecting several specialized TNN. The results of several tests, in different conditions are reported in the paper. The system detects accurately a test shape in almost all the experiments performed. The paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and was configured as a perceptron network with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits