Information Sharing via Projection Function for Coexistence of Robot and Human

Yujin Wakita, Shigeoki Hirai, Takashi Suehiro, Toshio Hori

Autonomous Robots, N0.10, 2001

Summary

                  The authors introduce the concept of safety based on intelligent augmentation of robotic systems. In previous studies the authors introduced the concept of tele-robotic systems (1992,1995,1996), where a robot is operated from another position with no physical contact and monitored through a television, and intelligent monitoring (1992), a system allowing conveyance of only required information through selection of data. The expansion of this last system has been the snapshot function (1995), where a laser pointer helps in teaching mode to estimate the deviation of the position, while the operator can move the robot, teaching the estimated relative deviation. A further implementation is the here proposed projection function (2001), where a robot and human jointly operate through a Digital Desk, a special environment provided with a projector perpendicular to the working table and a speaker. The aim of this research is to achieve intelligent augmentation in order to prevent and avoid undesirable contact, information sharing is a fundamental aspect in cooperative tasks between a person and a robot (Wakita, 1998). The experiment test a human and robot operating in mainly 5 states (initial, approach, grasp, release and final), the main issue is this kind of problem to be solves are: the person does not know the delivery coordinate, the person must keep holding the object until it is released, the person might be frightened by the robot movement.

The projection function consists of projecting on the table the simulated images of the moving robot, so that the human operator knows in real time the robots trajectory and understand the delivery trajectory. Force sensors in the robot’s fingers are used in order to allow the robot understand when the object has been grasped by the operator. A new teaching method also is introduced: the operator activated the teaching mode by touching the robot’s hand, then, instead of physically moving the manipulator, the projected image of the robot follows the operator’s hand to destination, the advantage is that only the model is required and no robot movement; the robot confirm through the speakers that the teaching trajectory has been saved.

The force sensors are an efficient communication method only during grasping, visual monitoring appears to be necessary for the entire delivery task.

It can be observed that humans in cooperation require visual feedback in order to understand that their motion and activity has been understood, each person expects to be observed during their action. So visual information appears to be extremely important by means of perception and it enhance safety in the system.

The digital desks comes to help once again in monitoring and indicating robots and humans in the system, in fact while operating a symbol (in the experiment it is a white rectangle) is projected on the hand of the operator when the robot has detected an action, in this way the human is aware that the robot knows about its presence.

In order to perform the experiment, a CCD camera was used for detection of human’s hand and robot position, and a video projector (SANYO LP-SG60) mounted on the ceiling in parallel with the camera.

The system as programmed, projects a white rectangle on the human’s hand when the CCD and the computer had performed the detection, while stationary hand is recognized a the delivery position.

Key Concepts

Human-Robot Interaction, Human-Robot Cooperation, Team Working

Key Results

The experiment appears to be useful prompting the importance of communication between robots and humans working together, a communication which need also visual feedback in order to ensure safety. A big part of communication is in fact performed not only by direct communication, but also by indirect feedback, showing that the message has been properly received. Future research may require adding information to the system.

Toward a Framework for a Human-Robot Interaction

Sebastian Thrun

Human-Computer Interaction, No.19, 2004

Summary

                  The field of robotics has undergone a considerable change from the time it first appeared as a complete science, robots now perform many assembly and transportation tasks, often equipped with minimal sensing and computing, slaved to perform a repetitive task. The future is more and more seeing the introduction of service robots and this is mainly thanks to reduce in costs of many technologies required and increase in autonomy capabilities.

Robotics appears to be a broad discipline and therefore definitions of this science are not unique, a general definition has been done by the author in a previous paper (Thrun, 2002) a system of robotic sensors, actuators and algorithms. The United Nations has categorized robotics in three fields: industrial robotics, professional service robotics and personal service robotics.

Industrial robotics are the earliest commercial success; an industrial robot operates manipulating its physical environment, it is computer controlled and operates in industrial settings (for example on conveyor belts).

Industrial robotics started in the 60s with the first commercial manipulator, the Unimate, later on in the 70s Nissan Corporation automated an entire assembly line with robots, starting a real “robotic revolution”, simply it can be considered that today the ration human to worker to robots is approximately 10:1 (the automotive industry is definitely the one with biggest application of robotics). However industrial robots are not intended to operate directly with humans.

Professional service robots are the younger kind of robots and are projected to assist people, perhaps in accessible environments or in tasks where speed and precision won’t definitely be met by human operators (as it is becoming more common in surgery).

Personal service robots posses today the highest expected growth rate, they are projected to assist people in domestic tasks and for recreational activities, often these robots are humanoids.

In all three of these fields two are the main drivers: cost and safety, these appear to be the challenges of robotics.

Autonomy refers to the ability the robot has to accommodate variation in the environment, it is a very important factory in human-robot interaction. Industrial robots are not considered to be highly autonomous, they often are called for repetitive tasks and therefore can be programmed, a different scenario appears to be the on of service robots where complexity of the environment brings them to be design to be very autonomous since they have to be able to predict the environment uncertainties, to detect and accommodate people and so on.

Of course there is also a cost issue, which necessitates the personal robots to be low-cost, therefore it they are the most complicated since the need high levels of autonomy and low costs. In human robot interaction extremely important become the interface mechanism, industrial robots are often limited, in fact they hard programmed and programming language and simulation softwares appear to be intermediary between the robot and the human. Service robots of course require richer interfaces and therefore distinguished are indirect and direct interaction methods. Indirect interaction consists of a person operating a robot through a command, while direct interaction consist of a robot taking decision on its on in parallel with a human.

Different technologies exist in order to achieve different method of communication, an interesting example appears to be the Robonaus (Ambrose et al., 2001), a master-slave idea demonstrating how a robot can cooperate with astronaut on a space station. Speech synthetisers and screens also appear to be interesting direct interaction methods.

Investigating humanoids and appearance, together with social aspect of service robots are also important aspect which researched are today investigating for the future of robotics.

Key Concepts

Human Robot Interaction, Human Robot Cooperation

Prediction of Human Behavior in Human – Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots

Tatsuya Nomura, Takayuki Kanda, Tomoshiro Suzuki, Kensuke Kato

IEEE Transactions on Robotics, Vol. 24, No. 2, April 2008

Summary

                  Robot are being capable to operate more and more together with humans, but still it may be into account that not all people may accept novel robots, as for Joinson (2002), opinions toward novel communication technologies tend to be highly polarized, so people might have negative attitudes and emotions toward novel robots. Studies have been done to understand the effects of human robot interaction on humans’ behavior, but non of them has addressed which kind of person might have difficulties in relating with robots and investigations on which kind of negative attitudes or emotions haven’t been done. The authors focus on anxiety and negative attitudes toward robots. In psychology attitude is defined as a relatively stable and enduring predisposition to behave in a certain way toward other people or element surrounding, while anxiety is defined as an apprehension state of the future about a specific fear. Anxiety is classified in trait anxiety (the trend of anxiety as a stable characteristic) and state anxiety (which is transiently evoked in specific situation that change according on situation and time). NARS is developed to determine human attitudes towards robots, the authors investigates mainly 3 possible states: S1) negative attitude toward social influence of robots; S2) Negative attitude toward the social influence of robots; S3) negative attitude toward emotional interaction with robots. A questionnaire has been performed (page 444). RAS is developed for measuring human anxiety toward robots evoked in real and imaginary HRI situations, in this case the these possible states issued are: S1) Anxiety toward communication capacity of robots; S2) Anxiety toward behavioral characteristics of robots; S3) Anxiety toward discourse with robots. Also in this case a questionnaire has been performed. Computer anxiety could be associated with robot anxiety, although it is a kind of state anxiety, while robot anxiety is only weakly correlated with state anxiety. These psychological scales in HRI may been useful, if we consider for example Friedman et. Al analyzed analyzed online discussion on the Aibo forum and found that people are not enthusiastic about a robotic dog, but are aware it’s a robot while interacting with it.  The authors performed an experiment on university student in Japan using “Robovie”, a human-like robot designed for communication with robots and provided with different kind of sensor systems. The experiment involved the subjects compile the questionnaire for NARS and RAS, walk into the room where the robot was and greet it, they were asked to enter in the room alone and move in front of the robot, then they had to talk for 30 seconds with the robot, after which Robotvie would interact with the subjects asking questions, after the answer the subjects would be asked to touch the robot. At the end of the experiment each subject was asked to respond the RAS once again to check differences with the previously answered survey. Parameters to be tested appear to be: D) distance from the robot at first sight; U1) time before subject talked after entering the room; U2) time after which the subject replied to the robot; T) time before the subject touched the robot.

Key Concepts

Human Robot Interaction, Effect of Robot on Human behavior

Key Results

The experiments shows that for that: male have positive relations between U1 and RAS-S3, T and RAS-S1, T and RAS-S2 and negative influence is between U1 and NARS-S1, T and NARS-S3 and also NARS-S2, although not statistically significant. For women positive influence is between D and RAS-S1 ad NARS-S1, NARS-S3 and U2 and negatively between D and NARS-S2. No significant different was therefore noted between genders. Subjects with emotional utterances toward robots were found with higher negative attitude and anxiety toward interaction with robots than those with no-emotional utterances. Some correlation per gender were find between NARS and RAS. The paper demonstrates influence attitude have from robotics. 

Whose Job is it anyway? A Study of Human-Robot Interaction in a Collaborative Task

Pamela J. Hinds, Teresa L. Roberts, Hank Jones

Human-Computer Interaction, Volume 19, 2004

Summary

                  Human Robot cooperation is growing more and more and researches have supposed that humans may prefer working with human-like robots than machine-like, although, according to the authors, no test has been down up to the paper’s date (2004). The paper researches links with human likeness, status (subordinate, peer or supervisor) and dimensions. Today researches are divided mainly in two “team”, according to Brooks [2002], humanoids will have better communication chances than machine-like robots, while opponents believe that humanoid features may result in unrealistic expectations and in some cases even fear. In this research the case of underreliance is faced, being proved (Gawande, 2002) that people tend to resist technologies that are programmed to augment human decision making. Another aspect covered in this research is the level of responsibility that people assume for a certain task in certain conditions and with a certain robot cooperator.

The authors performed statistical test on 5 hypothesis: 1a) People rely on human-like robot partner more than a machine-like one; 1b) People will feel less responsible for the task when collaborating with a human like robot partner than a machine-like one; 2a) People will rely on the robot partner more when its characterized as a supervisor than when it is characterized as a subordinate; 2b) People will feel less responsible for a task when collaborating with a robot partner who is a supervisor than with a robot partner who is a subordinate or a peer; 3) People will feel the greatest amount of responsibility when collaborating with a machine-like robot subordinates as compared with machine-like robot subordinated. To test the tree hypothesis the researchers performed experiments to verify human likeness and status influence in human perception, the robot was operating in Wizard of Oz conditions (teleoperated) without the people performing been told.

The experiments have been performed with a the same robot, once wearing human-like features such as nose, ears, mouth and eyes been demonstrated (Di Salvo, Gemperle, Forlizzi and Kiesler, 2002) that there are the characteristics that most affect perception of human-likeness; the status has been previously communicated to the testers through written instruction (as been successfully done previously by Sande, 1986).

The experiment analyzed, through videotapes analysis, the attribution of credit and blame, specially using the concept of shared social identity analyzing the language used by the testers while working together with the robot.

Key Concepts

Human-Robot Cooperation, Team-working, Humanoids, Robot impact on humans

Key Results

The results have shown multiple aspects, first of all, not unexpected is the preference humans have in working with other humans rather than robots, but the difference regarding responsibility, attribution of blame and attribution of credit appears to be not statistically significant, as for the difference between human-like robot and machine-like robot. Hypothesis 1a and 1b appear therefore to be confirmed. It is interesting to notice how users tend communicated more with machine-like robots, since people perceive less common ground between themselves and the robot (Fussel & Krauss, 1992). Also it has been proved that people relied more on a peer robot than a subordinate or supervisor robot (when the robot is a supervisor then humans tend to blame the mistakes and attribute to themselves the success) and people feel much more responsible for the task when cooperating with a machine-like robot. This results suggests that the appearance of the robot is important according on the degree of responsibility required, when it’s needed to have more options then it would be better to have a machine-like robot (Robert et al., 1994), in the case of high hazardous environment and risk then humanoids may be a good choice so that people may delegate easily responsibilities to them.

Safety Issues for Human-Robot Copperation in Manufacturing Systems

Agostino De Santis, Bruno Siciliano

Tools and Perspectives in Virtual Manufacturing, July 10^th 2008 – July 11^th 2008

Summary

                  The next generation robots will be enhanced with the challenging and revolutionary feature of physical Human Robot Interaction (pHRI), making safety and dependability key concepts of the future of robotics.

Safety, being fundamental for future HRI, appears to cover several aspect, such as: mechanics, electronics and software. Up to now safety has force robots to be segregated from the operators working environment, cHRI (cognitive Human Robot Interaction) has been commonly debated in the scientific community, however robots are distinct from computers and other machines: they generate force and have “body”, making it be an intelligent connection in the direction of pHRI, allowing more and more safe, fast and accurate motions without third-party sensors.

The new robots are then considered under two criteria: safety (which includes also “mental safety”, which is the awareness of robot motion) and dependability (which allows “human-in-the-loop” conditions). Standards at the moment are not yet ready for the share of operational space, work is being going on the international ISO 10218 in order to include aspect on robot’s work place.

It is clear that in order to allow humans and robots to share common working environment, metrics regarding safety levels must be introduce and not surprisingly measures already use in the automotive sector are currently used. Two methods are applied: direct interaction (for head collision with another solid object) and indirect interaction (for sudden head motion with no direct contact). The scaling used (AIS – Abbreviated Injury Scale) is indicating the injury severity on the overall injury (MAIS); injury types are then divided in a classification related to type and consequences and is based on an ascending scale from 0 to 6. Paradoxically the result from this metric is that it enourages the usa of robots, since it is considered that even withouth the use of robots operator get injured. Safety can be taken with two different approaches: intrinsic safety and actuation (for example a distribuition macromini actuation DM² [Zinn,2002], where for each d.o.f. a pair of actuators connected in parallel and located in different parts on the manipulator, ensuring lower inertia and good performance; VIA – Variable Impendance Approach, is a mechanical control co-design that allows varying stiffness, damping and gear-ratio in order to minimize the negative effects of control performance) and safety by mean of control (which could be either position controlled or force/impendance control, the latter being direct in the case of a feedback loop control or indirect with a motion control loop). In the case of safety by mean of control, we my consider reactive control obtained through potential fields, which have the mission of creating attracting or repelling volumes, an example is the skeleton algorithm, which creates rotational volumes in proximity of links, this created a virtual region which approaches the real volume of a considered part of a manipulator.

The algorithm allows to use the human head avoidance illustrated previously and in case of a safe robot, the additional safety can be considered to be enough.

The issues which the authors encounted are regarding communication, in fact a emergency stop of the robot still has to be tested in case of particular necessities, but still it appears to be a fast modelling method.

The importance of simulation useing virtual reality is underlined, since it provides and ergonomic evaluation, comfort measure and of course a simulation of possible and eventual malfuctioning, allowing a fast comparison of interface, appearance and kinematic parameters.

Key Concepts

Safety, Virtual Reality, Simulation, Reactive Control, skeleton algorithm, physical Human Robot Interaction.