Human–robot skills transfer interfaces for a flexible surgical robot

In minimally invasive surgery, tools go through narrow openings and manipulate soft organs to perform surgical tasks. There are limitations in current robot-assisted surgical systems due to the rigidity of robot tools. The aim of the STIFF-FLOP European project is to develop a soft robotic arm to perform surgical tasks. The flexibility of the robot allows the surgeon to move within organs to reach remote areas inside the body and perform challenging procedures in laparoscopy. This article addresses the problem of designing learning interfaces enabling the transfer of skills from human demonstration. Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level imitation of the underlying intent extracted from the demonstrations. By focusing on this last form, we study the problem of extracting an objective function explaining the demonstrations from an over-specified set of candidate reward functions, and using this information for self-refinement of the skill. In contrast to inverse reinforcement learning strategies that attempt to explain the observations with reward functions defined for the entire task (or a set of pre-defined reward profiles active for different parts of the task), the proposed approach is based on context-dependent reward-weighted learning, where the robot can learn the relevance of candidate objective functions with respect to the current phase of the task or encountered situation. The robot then exploits this information for skills refinement in the policy parameters space. The proposed approach is tested in simulation with a cutting task performed by the STIFF-FLOP flexible robot, using kinesthetic demonstrations from a Barrett WAM manipulator.     

I robot，again

大家一定很奇怪,我为什么要说“又”（again）呢？因为,在很久很久以前（大概是两年以前了吧）,咱们“DF触觉”栏目就为大家带来过一篇名为《I robot》的文章。不过,当时介绍的,都是玩具类的机器人。而这一次,我们要给大家带来的,是几款真正能融入人类生活,真正能为人类提供帮助的机器人。     

Markerless human–robot interface for dual robot manipulators using Kinect sensor

Remote teleoperation of robot manipulators is often necessary in unstructured, dynamic, and dangerous environments. However, the existing mechanical and other contacting interfaces require unnatural, or hinder natural, human motions. At present, the contacting interfaces used in teleoperation for multiple robot manipulators often require multiple operators. Previous vision-based approaches have only been used in the remote teleoperation for one robot manipulator as well as require the special quantity of illumination and visual angle that limit the field of application. This paper presents a noncontacting Kinect-based method that allows a human operator to communicate his motions to the dual robot manipulators by performing double hand–arm movements that would naturally carry out an object manipulation task. This paper also proposes an innovative algorithm of over damping to solve the problem of error extracting and dithering due to the noncontact measure. By making full use of the human hand–arm motion, the operator would feel immersive. This human–robot interface allows the flexible implementation of the object manipulation task done in collaboration by dual robots through the double hand–arm motion by one operator.     

Mental tasks-based brain–robot interface

This paper describes a Brain Computer Interface (BCI) based on electroencephalography (EEG) that allows control of a robot arm. This interface will enable people with severe disabilities to control a robot arm to assist them in a variety of tasks in their daily lives. The BCI system developed differentiates three cognitive processes, related to motor imagination, registering the brain rhythmic activity through 16 electrodes placed on the scalp. The features extraction algorithm is based on the Wavelet Transform (WT). A Linear Discriminant Analysis (LDA) based classifier has been developed in order to differentiate between the three mental tasks. The classifier combines through a score-based system four LDA-based models simultaneously. The experimental results with six volunteers performing several trajectories with a robot arm are shown in this paper.     

Can a robot empathize with people?

This article explores a robotogenetic model of empathetic understanding of another mind as one of the capabilities required in human–robot social interactions. The term robotogenetic means that we implement a possible ontogeny (i.e., developmental process) of the social capability onto a robotic embodiment with a certain phylogenetic background (i.e., innate prerequisites). First, we look into infants development of social and communicative skills, especially of empathetic understanding of others. We then consider two fundamental abilities, namely eye-contact and joint attention, as the prerequisites for this cognitive development. Then in psychological experiments using robots that are capable of eye-contact and joint attention, we observe how people, especially infants and children, attribute mental states to the robots. Based on these investigations, we consider a possible mechanism of empathy which is based on the spatiotemporal coordination of attention and bodily movement between the self and another.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Perception of own and robot engagement in human–robot interactions and their dependence on robotics knowledge

Communication between socially assistive robots and humans might be facilitated by intuitively understandable mechanisms. To investigate the effects of some key nonverbal gestures on a human’s own engagement and robot engagement experienced by humans, participants read a series of instructions to a robot that responded with nods, blinks, changes in gaze direction, or a combination of these. Unbeknown to the participants, the robot had no form of speech processing or gesture recognition, but simply measured speech volume levels, responding with gestures whenever a lull in sound was detected. As measured by visual analogue scales, engagement of participants was not differentially affected by the different responses of the robot. However, their perception of the robot’s engagement in the task, its likability and its understanding of the instructions depended on the gesture presented, with nodding being the most effective response. Participants who self-reported greater robotics knowledge reported higher overall engagement and greater success at developing a relationship with the robot. However, self-reported robotics knowledge did not differentially affect the impact of robot gestures. This suggests that greater familiarity with robotics may help to maximise positive experiences for humans involved in human–robot interactions without affecting the impact of the type of signal sent by the robot.     

Bootstrapping a robot’s kinematic model

We present a system that is able to autonomously build a 3D model of a robot’s hand, along with a kinematic model of the robot’s arm, beginning with very little information. The system starts by using exploratory motions to locate and centre the robot’s hand in the middle of its field of view, and then progressively builds the 3D and kinematic models. The system is flexible, and easy to integrate with different robots, because the model building process does not require any fiducial markers to be attached to the robot’s hand. To validate the models built by the system we perform a number of experiments. The results of the experiments demonstrate that the hand model built by the system can be tracked with a precision in the order of 1 mm, and that the kinematic model is accurate enough to reliably position the hand of the robot in camera space.     

Network-based reconfiguration routes for a self-reconfigurable robot

This paper presents a network-based analysis approach for the reconfiguration problem of a self-reconfigurable robot. The self-reconfigurable modular robot named ＂AMOEBA-I＂ has nine kinds of non-isomorphic configurations that consist of a configuration network. Each configuration of the robot is defined to be a node in the weighted and directed configuration network. The transformation from one configuration to another is represented by a directed path with nonnegative weight. Graph theory is applied in the reconfiguration analysis, where reconfiguration route, reconfigurable matrix and route matrix are defined according to the topological information of these configurations. Algorithms in graph theory have been used in enumerating the available reconfiguration routes and deciding the best reconfiguration route. Numerical analysis and experimental simulation results prove the validity of the approach proposed in this paper. And it is potentially suitable for other self-reconfigurable robots＇ configuration control and reconfiguration planning.     

The development of an autonomous service robot. Implementation: “Lucas”—The library assistant robot

This paper describes an autonomous mobile device that was designed, developed and implemented as a library assistant robot. A complete autonomous system incorporating human–robot interaction has been developed and implemented within a real world environment. The robotic development is comprehensively described in terms of its localization systems, which incorporates simple image processing techniques fused with odometry and sonar data, which is validated through the use of an extended Kalman filter (EKF). The essential principles required for the development of a successful assistive robot are described and put into demonstration through a human–robot interaction application applied to the library assistant robot.     

Efficient behavior learning in human–robot collaboration

We present a novel method for a robot to interactively learn, while executing, a joint human–robot task. We consider collaborative tasks realized by a team of a human operator and a robot helper that adapts to the human’s task execution preferences. Different human operators can have different abilities, experiences, and personal preferences so that a particular allocation of activities in the team is preferred over another. Our main goal is to have the robot learn the task and the preferences of the user to provide a more efficient and acceptable joint task execution. We cast concurrent multi-agent collaboration as a semi-Markov decision process and show how to model the team behavior and learn the expected robot behavior. We further propose an interactive learning framework and we evaluate it both in simulation and on a real robotic setup to show the system can effectively learn and adapt to human expectations.     

用C#实现带身份验证的robot程序

网络机器人是搜索引擎中的核心部分,其首要步骤是获取页面内容来分析和追踪其包含的链接,以便搜索更多的网页.出于某些安全和商业因素,有些网站的相关内容必须要在有效用户登录后才可以查看,所以其相关页面的下载,首先需要通过身份验证.这就需要使用HTTP用户认证机制来判别用户身份,本文主要论述了怎么样用C#来实现通过HTTP用户认证实现页面下载.     

A human–robot interface for mobile manipulator

This paper presents a remote manipulation method for mobile manipulator through operator’s gesture. In particular, a track mobile robot is equipped with a 4-DOF robot arm to grasp objects. Operator uses one hand to control both the motion of mobile robot and the posture of robot arm via scheme of gesture polysemy method which is put forward in this paper. A sensor called leap motion (LM), which can obtain the position and posture data of hand, is employed in this system. Two filters were employed to estimate the position and posture of human hand so as to reduce the inherent noise of the sensor. Kalman filter was used to estimate the position, and particle filter was used to estimate the orientation. The advantage of the proposed method is that it is feasible to control a mobile manipulator through just one hand using a LM sensor. The effectiveness of the proposed human–robot interface was verified in laboratory with a series of experiments. And the results indicate that the proposed human–robot interface is able to track the movements of operator’s hand with high accuracy. It is found that the system can be employed by a non-professional operator for robot teleoperation.     

Development of the quadruped walking robot,TITAN-IX — mechanical design concept and application for the humanitarian de-mining robot

This paper proposes a quadruped walking robot that has high performance as a working machine. This robot is needed for various tasks controlled by tele-operation, especially for humanitarian mine detection and removal. Since there are numerous personnel landmines that are still in place from many wars, it is desirable to provide a safe and inexpensive tool that civilians can use to remove those mines. The authors have been working on the concept of the humanitarian demining robot systems for 4 years and have performed basic experiments with the first prototype VK-I using the modified quadruped walking robot, TITAN-VIII. After those experiments, it was possible to refine some concepts and now the new robot has a tool (end-effector) changing system on its back, so that by utilizing the legs as manipulation arms and connecting various tools to the foot, it can perform mine detection and removal tasks. Toaccomplish these tasks, we developed various end-effectors that can be attached to the working leg. In this paper we will discuss the mechanical design of the new walking robot called TITAN-IX to be applied to the new system VK-II.     

How social distance shapes human–robot interaction

This paper investigates how social distance can serve as a lens through which we can understand human–robot relationships and develop guidelines for robot design. In two studies, we examine the effects of distance based on physical proximity (proxemic distance), organizational status (power distance), and task structure (task distance) on people׳s experiences with and perceptions of a humanlike robot. In Study 1, participants (n=32) played a card-matching game with a humanlike robot. We manipulated the power distance (supervisor vs. subordinate) and proxemic distance (close vs. distant) between participants and the robot. Participants who interacted with the supervisor robot reported a more positive user experience when the robot was close than when the robot was distant, while interactions with the subordinate robot resulted in a more positive experience when the robot was distant than when the robot was close. In Study 2, participants (n=32) played the game in two different task distances (cooperation vs. competition) and proxemic distances (close vs. distant). Participants who cooperated with the robot reported a more positive experience when the robot was distant than when it was close. In contrast, competing with the robot resulted in a more positive experience when it was close than when the robot was distant. The findings from the two studies highlight the importance of consistency between the status and proxemic behaviors of the robot and of task interdependency in fostering cooperation between the robot and its users. This work also demonstrates how social distance may guide efforts toward a better understanding of human–robot interaction and the development of effective design guidelines.     

Center-configuration selection technique for the reconfigurable modular robot

The reconfigurable modular robot has an enormous amount of configurations to adapt to various environments and tasks. It greatly increases the complexity of configuration research in that the possible configuration number of the reconfigurable modular robot grows exponentially with the increase of module number. Being the initial configuration or the basic configuration of the reconfigurable robot, the center-configuration plays a crucial role in system’s actual applications. In this paper, a novel center-configuration selection technique has been proposed for reconfigurable modular robots. Based on the similarities between configurations’ transformation and graph theory, configuration network has been applied in the modeling and analyzing of these configurations. Configuration adjacency matrix, reconfirmation cost matrix, and center-configuration coefficient have been defined for the configuration network correspondingly. Being similar to the center-location problem, the center configuration has been selected according to the largest center-configuration coefficient. As an example of the reconfigurable robotic system, AMOEBA-I, a three-module reconfigurable robot with nine configurations which was developed in Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), has been introduced briefly. According to the numerical simulation result, the center-configuration coefficients for these nine configurations have been calculated and compared to validate this technique. Lastly, a center-configuration selection example is provided with consideration of the adjacent configurations. The center-configuration selection technique proposed in this paper is also available to other reconfigurable modular robots. Supported in part by the National High-Technology 863 Program (Grant No. 2001AA422360), the Chinese Academy of Sciences Advanced Manufacturing Technology R&D Base Fund (Grant Nos. A050104 and F050108), and the GUCAS-BHP Billiton Scholarship     

Adaptation of robot’s perception of fuzzy linguistic information by evaluating vocal cues for controlling a robot manipulator

This article proposes a method for adapting a robot’s perception of fuzzy linguistic information by evaluating vocal cues. The robot’s perception of fuzzy linguistic information such as “very little” depends on the environmental arrangements and the user’s expectations. Therefore, the robot’s perception of the corresponding environment is modified by acquiring the user’s perception through vocal cues. Fuzzy linguistic information related to primitive movements is evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues for modifying the BEN. The user’s satisfactory level for the robot’s movements and the user’s willingness to change the robot’s perception are identified based on a series of vocal cues to improve the adaptation process. A situation of cooperative rearrangement of the user’s working space is used to illustrate the proposed system by a PA-10 robot manipulator.     

How will a robot change your life?

Any way you look at it, the robot has an exciting future. Soon, we can expect the robot to move out of the factory and enter the domestic and business worlds. The domestic robot will appear in the home as an electronic pet and soon will develop the ability to perform useful tasks there. The sensory ability of all robots will greatly improve. In the long run, robots will acquire the capabilities they have been described as having in the movies and science fiction books. Self-reproducing factories may be placed on the moon or on other planets to help meet our growing needs for energy and goods. Inspirational changes are on the way as robots become the helpers that humans have always dreamed of     

Skill-based human–robot cooperation in tele-operated path tracking

This work proposes a shared-control tele-operation framework that adapts its cooperative properties to the estimated skill level of the operator. It is hypothesized that different aspects of an operator’s performance in executing a tele-operated path tracking task can be assessed through conventional machine learning methods using motion-based and task-related features. To identify performance measures that capture motor skills linked to the studied task, an experiment is conducted where users new to tele-operation, practice towards motor skill proficiency in 7 training sessions. A set of classifiers are then learned from the acquired data and selected features, which can generate a skill profile that comprises estimations of user’s various competences. Skill profiles are exploited to modify the behavior of the assistive robotic system accordingly with the objective of enhancing user experience by preventing unnecessary restriction for skilled users. A second experiment is implemented in which novice and expert users execute the path tracking on different pathways while being assisted by the robot according to their estimated skill profiles. Results validate the skill estimation method and hint at feasibility of shared-control customization in tele-operated path tracking.     

3-D Locomotion control for a biomimetic robot fish

This paper concerns with 3-D locomotion control methods for a biomimetic robot fish. The system architecture of the fish is firstly presented based on a physical model of carangiform fish. The robot fish has a flexible body, a rigid caudal fin and a pair of pectoral tins, driven by several servomotors. The motion control of the robot fish are then divided into speed control, orientation control, submerge control and transient motion control, corresponding algorithms are detailed respectively. Finally, experiments and analyses on a 4-link, radio-controlled robot fish prototype with 3-D locomotion show its good performance.