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Apocalypse Gylden Kongerike. Next page. There's a problem loading this menu right now. Learn more about Amazon Prime. Get fast, free delivery with Amazon Prime. Books By J. Get it by Wednesday, Oct Out of Print--Limited Availability. Apocalypse Gylden Kongerike Nov 4, Apocalypse Gm's Campaign Guide Oct 30, More Information. While pattern recognition approaches are appealing for their ability to learn arbitrary mappings between myographic signal features and intent information, they are most commonly used to select from a relatively small number of discrete control states.
For applications such as neurorehabilitation, where sometimes the goal is only to trigger the appropriate robot motion at the appropriate time, this is acceptable. However, other applications, including other modes of neurorehabilitation, would benefit from extracting continuously time-varying information such as the user's desired joint torques. A simple approach to this problem is to match the EMG signal amplitudes of agonist—antagonist muscle pairs to antagonistic cable actuation systems for rotary exoskeleton joints, and then to hand tune a proportional gain from the EMG signal, postprocessing, to the assistive torque provided by the robot [ 33 ].
The assumption here is that subjects that have been weakened by neurological damage will benefit from a robot providing torque that is in the same direction and roughly proportional to the user's desired torque. Lenzi et al. While this approach has the potential to greatly simplify the design of control systems for powered exoskeletons, it has the significant drawback of disturbing a user's natural motor function at the neural level.
Other researchers have worked to develop control systems that explicitly learn the mapping between EMG signals and user's desired joint torque. An excellent example by Kiguchi and Hayashi is the use of an adaptive neuro-fuzzy modifier to learn the relationship between the root-mean-square of measured EMG signals and the estimated user's desired torques [ 30 ]. The approach is to use an error-backpropagation learning algorithm to modify the mapping, which is expressed as a weighting matrix.
The neuro-fuzzy modifier takes as inputs the joint angle measurements provided by the robot in order to account for the effect of varying limb position on EMG signals. Examples of lower-limb exoskeletons from previous decades, which are covered more extensively in Ref. Once again, the estimated joint torque at the knee has a hand-tunable scaling factor applied to produce the commanded actuator torque, under the assumption that the user's desired torque is reflected accurately in the torque they are able to generate.
When designing controllers for mechanical systems, the variable to be controlled is often position and the variable representing the controller effort is force. It is then no surprise that in applications of human—robot shared control systems, the user intent is often defined as the force generated by the user. Consequently, many examples of user intent detection revolve around estimating the user contribution to the interaction force. This user-intended force is then subtracted from the robot controller effort so that it assists the user minimally in achieving the predefined trajectory.
Instead of measuring robot position to estimate interaction force, one can measure interaction force between the human and the robot at the end effector and estimate the desired human position. Ge et al. The user's intended position is then assumed to be the equilibrium point of the spring. The authors use a radial basis function RBF neural network, which has the property of universal functional approximation, to learn an estimate of the human dynamics.
Li and Ge [ 26 ] extend their previous work so that the synaptic weight vector of the neural network can be updated in real time to respond to changing human impedance. Just as researchers have used interaction force measurements to estimate a desired position of the human [ 25 , 26 ], the measured interaction force can be used to estimate other forms of motion intention.
For example, in Ref. The user's state is a discrete variable representing possible walking modes, e. The detection of the walking state is paired with the use of a Kalman filtering KF technique—based on the forward dynamics of the cane robot—to estimate the direction and magnitude of the user's desired acceleration. Finally, Erden and Tomiyama [ 27 ] present a unique interpretation of human intent obtained from the measured interaction force between a human hand and a HapticMaster robot. The robot is under impedance control; thus, using the principle of preservation of momentum, it follows that the integral of the controller force applied by the robot in the time period between two stable resting states is equal to the total momentum delivered by the human interaction.
Therefore, the authors use the integral of the robot controller force—which can also be called the impulse—as a measurement of the human intention and define a user's desired change in set point position of the robot as being proportional to the impulse by some tunable scaling factor. This final relationship is based solely on an intuitive understanding of the load dynamics and the ways in which humans tend to manipulate objects. It is a convenient substitution, since relating the impulse to the desired set point position means the intent can easily be given as an input to the robot impedance controller.
Another paradigm for intent interpretation involves the use of robot position measurement to predict future motion. Corteville et al. Position sensing is used to estimate a minimum jerk, bell-shaped profile of the user's desired speed that is continuously updated. The minimum-jerk speed trajectory has been in used by many researchers as a model for human movements [ 36 ].
Under the same paradigm, Brescianini et al. From these signals, the authors extract gait parameters such as stride length, height difference, direction, and operation mode. These values are then used to generate motion trajectories for a lower-limb exoskeleton that is being worn along with the crutches.
The final example of intent interpretation we will examine is the use of force and position to estimate human impedance. Wang et al. The robot end effector is simply a metal rod that a human may grasp as if it is the other partner in a hand shake. An impedance relationship can be defined between the measured position and orientation of the robot end effector and the resulting forces and torques measured at the end effector, resulting from interaction with the human. The human is then modeled as a linear impedance with three parameters—mass, damping, and stiffness.
The authors have made extensive use of their model of the human control, i.
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Arbitration here refers to the division of control among agents when attempting to accomplish some task. More specifically, during physical human—robot interaction with shared control, arbitration determines how control is divided between the human and robot. Many different types of arbitration are possible; for instance, the human might be responsible for controlling the position of the robot's end effector, while the robot controls the end-effector's orientation. Alternatively, both the human and robot could be jointly responsible for the position and orientation of the robot's end effector, but have different relative levels of influence.
A simple example of this kind of arbitration for shared control is shown in Fig.
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Clearly, whenever the human and robot are both actively working together to accomplish some task, arbitration of some sort is either implicitly or explicitly present. An understanding of arbitration within physical human—robot interaction is therefore necessary for shared control. In fact, arbitration—in some form or fashion—has already been described as an integral part of human—robot interaction, even outside the field of shared control. On the one hand, to reduce the human's burden, we generally want the robot to be as autonomous as possible.
On the other hand, however, we note that autonomy in shared control is limited by Bratman's commitment to mutual support, since, if the completely autonomous robot ignores the human's intent altogether, there is no opportunity to work collaboratively or offer assistance. So as to resolve this conflict and better understand arbitration within our context of shared control, we briefly turn to recent studies of physical human—human dyads, where two humans are working together to accomplish the same task.
Reed and Peshkin [ 38 ] examined physical human—human interaction during a simple 1DoF task, and focused on haptic communication of information see Fig. They found—like other researchers—that dyads of humans working together completed the task more quickly than a single individual working alone, and, of particular interest, they discovered that humans naturally assume different roles during task execution.
Although this phenomenon is not yet fully understood, subsequent work by Ueha et al. Here, the authors found that one human naturally assumed control of the tangential forces, which are related to larger motions, while the second human took control of the radial forces, which are related to finer positioning; again, a natural arbitration of roles emerges. Finally, Feth et al.
Like in Ref. Moreover, these studies of human—human dyads found that roles varied dynamically over time so that a human who once served as leader could become a follower, or, by the same process, the human who had assumed a follower role could transition into leadership. Viewed together, these concepts of arbitration in cooperative activity and studies of arbitration within human—human dyads suggest two fundamental questions for arbitration and shared control: a how should roles be allocated and b how should these roles be dynamically updated?
This review is not meant to be comprehensive, and will almost certainly omit several exciting works; the works we have included, however, are meant to provide the reader with a sense of the complexities and benefits associated with the different types of static and dynamic role arbitration. The authors argue that both the human agent and the robotic agent have an inherent cost function, which consists of a sum of error and effort components, and each agent naturally attempts to minimize their individual cost function at a Nash equilibrium.
By error, we here mean a difference in position or orientation with respect to the agent's desired trajectory or goal pose, and by effort, we mean the amount of force, torque, or muscle activation, which an agent applies during interaction. These four types of role allocation are distinguished by differences in the robotic cost functions; the human is always assumed to minimize their own perceived error and effort. Within co-activity the task is divided into subtasks, and the human and robot are assigned unique subtasks which can be completed independently.
In this case, the cost associated with an agent is a function of that agent's own error and effort, and when one agent changes his or her error or effort, it does not directly alter the cost of the other agent. By contrast, in a master—slave role allocation, both agents are attempting to complete the same task, and the cost of the robot is defined to be the sum of the human's error and effort. Therefore, the robot will here exert as much effort as possible to minimize the human's error and effort without any regard for the robot's own error and effort, i.
Accordingly, the robot considers its own effort, and gradually attempts to reduce its effort as the human performs more of the task independently, i. This role allocation for pHRI is most similar to the human—human dyads previously discussed. We will now attempt to classify examples of arbitration in shared control based on these four types of role allocation, noting that some research is difficult to place in a single category because it contains elements of multiple role allocation types.
To begin, we consider co-activity, or a division of subtasks, which is particularly applicable when there are aspects of the task that one agent is unable to perform. Perhaps, the most illustrative instances of this can be found in powered prosthetics, where the human is necessarily unable to actuate the prosthesis herself, and must instead rely on the prosthesis correctly carrying out their communicated intent.
Work by Varol et al. These authors assume that the human wants their prosthesis to be in one of three different states—either sitting, standing, or walking—and the authors have also developed automated transitions between these states. Given the robot's interpretation of the human's intent to sit, stand, or walk, the robot transitions to or remains in the most appropriate state. Hence, the roles are allocated such that the human's subtask is to decide on the desired state, and the robot's subtask is to execute the motions relevant to that state.
It is assumed in Ref. Myoelectric control of upper-limb powered prostheses likewise relies on the robot performing the human's desired motions. In this case, the main difficulty is leveraging signals generated by the human's muscles in order to control artificial hands that often possess a high number of actuated DoF. Typically, this problem is resolved with pattern recognition techniques [ 43 , 44 ].
While classification results can be reasonably accurate, users have reported that the prosthesis is still difficult to control [ 45 ], and so this process remains an open avenue for research. Interestingly, while upper-limb prostheses generally try to leave full control to the user, they can also include some levels of autonomy, such as for lower level slip prevention tasks [ 11 ]. Along these same lines, we should also quickly mention brain controlled wheelchairs [ 46 — 48 ], where the human again has a higher level decision task, and the robot is responsible for a lower level navigation subtasks.
Philips et al. A similar scheme is presented by Carlson and Millan [ 47 ], where by default, the wheelchair moves forward while avoiding obstacles, and the human communicates the arbitrated intention to move right or left. The subtask of the robot can further be expanded to include holistic navigation; in Rebsamen et al. Co-activity in shared control, however, is not limited simply to applications where the human is physically unable to carry out certain aspects of the task.
Aarno et al. It may be possible for the human to perform the teleoperation or co-manipulation task completely alone, but the inclusion of these subtasks, and a probabilistic estimation of the current subtask, was found to improve the human's performance. Indeed, since the human is involved in all aspects of the task's execution, this research combines components of both co-activity the delegation of subtasks and master—slave arbitration virtual fixtures along those subtasks. The master—slave role allocation, with human masters and robotic slaves, is likely the most traditional and ubiquitous type of role arbitration in shared control.
Utilizing impedance control, for instance, a robot can follow the desired trajectory without any human participation, while still responding naturally to external perturbations [ 9 ]. Moreover, we would point out that in some sense, the human master and robot slave arbitration should always be present within shared control, because, for safety purposes, the human must always retain final authority during situations where the human and robot are in conflict [ 9 ].
Abbott et al. Although Refs. Virtual constraints have primary applications in surgical teleoperation and co-manipulation, and can deal with situations where certain areas of the workspace are out of bounds forbidden-region virtual fixtures or where the human seeks to follow a desired trajectory guidance virtual fixtures. Of special interest for our study of arbitration are the unique works on virtual fixtures by Yu et al. Yu et al. Li and Okamura [ 53 ] provide a methodology for the robot to discretely switch the virtual fixture on or off, depending on the human's communicated intent.
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Practically, turning off the virtual fixture provides humans the freedom to the leave the desired trajectory when seeking to avoid unexpected obstacles. Theoretically, removing the virtual fixture amounts to shifting from a master—slave role allocation to a role allocation where the human completes the task alone, in other words, the first level of autonomy as described by Goodrich and Schultz [ 1 ]. Interestingly, this switch between discrete master—slave and single-agent role allocations is also prevalent in rehabilitation robotics studies; for example, see Mao and Agrawal [ 54 ].
These authors implement a virtual tunnel around the desired trajectory, within which only a constant tangential force is applied to help keep the human moving see Fig. If the human accidentally moves outside of the tunnel, however, a master—slave role allocation is invoked, and the robot uses impedance control to correct the human's positional error. Another combination of master—slave and single-agent role allocation for rehabilitation applications is offered by Duschau-Wicke et al. Here, the human is completely responsible for the timing of their motions—without robotic assistance—but the robot uses impedance control to constrain positional errors with respect to the given path.
Fortunately, this is not an issue for surgical applications, where the human's involvement is required due to safety concerns [ 51 ].
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On the other hand, master—slave role allocations can undesirably de-incentivize human participation during rehabilitation applications [ 56 ], which, in turn, leads to both the combinations of master—slave and single-agent arbitration that we have discussed, as well as teacher—student role allocations. The teacher—student role arbitration is well suited for situations where we are attempting to train humans using robotic platforms [ 57 ], which, considering the application areas focused on in this review, primarily entails robotic rehabilitation.
The teacher—student role allocation is distinguished from discrete combinations of master—slave and single-agent arbitration, since teacher—student role arbitrations constantly attempt to reduce the amount of robotic effort. As explained by Blank et al. Shared control strategies, which employ the teacher—student role arbitration in the field of rehabilitation robotics, are typically referred to as assist-as-needed AAN controllers.
In other words, as argued by Wolbrecht et al. Since, as experimentally shown by Emken et al. In practice, teacher—student role arbitration is often effected by starting with a master—slave role arbitration, and then reducing the robot's effort whenever possible. Our group has employed this technique in Ref. Hence, as the human grows more adept at the task over time, the initial master—slave impedance controller can gradually become a single-agent role allocation, where the human is responsible for performing the task alone.
A similar instantiation of the teacher—student role allocation may also be achieved using forgetting factors, such as in Wolbrecht et al. Again, the arbitration here gradually shifts from master—slave—where the robot can complete the task even with an unskilled or passive human operator—to a single-agent arbitration—where the human must complete the task without any assistance. It should be understood, however, that teacher—student role allocation does not monotonically shift from master—slave to single agent; the mentioned works [ 28 , 58 , 60 ] all incorporate features that can increase the robot's teacher's assistance when the human student is regressing.
Extending this line of reasoning, Rauter et al. Perhaps the teacher—student role arbitration, particularly in AAN or rehabilitation applications, is best characterized as a dynamic role arbitration, where the robot's role continuously adjusts from more helpful slave to less helpful uninvolved or antagonistic depending on the human's motor learning and participation. Collaborative arbitration for shared control is thus similar to teacher—student arbitration, because the equitable relationship between human and robot implied by collaboration also leads to very changeable, or dynamic, roles.
Unlike teacher—student arbitration, however, which we discovered to be applied primarily within rehabilitation, collaborative arbitration has more general application areas. The majority of papers surveyed below [ 10 , 62 — 65 ] use collaborative arbitration for co-manipulation tasks, where the human and robot are both grasping a real or virtual object, and together are attempting to move that object along a desired trajectory, or place that object in a desired goal pose. We might imagine, for example, a human and robot moving a table together.
Alternatively, work by Dragan and Srinivasa [ 66 ], which includes a brief review of arbitration, atypically develops a collaborative arbitration architecture for teleoperation applications. Here, the human's inputs are captured using motion tracking, from which the robotic system probabilistically estimates the human's desired goal via minimum entropy inverse reinforcement learning. The robot then arbitrates between the inputs of the human and its own prediction of the human's goal in order to choose the manipulator's motion. We note that for both co-manipulation and teleoperation applications of collaborative arbitration, the robot is theoretically meant to act as a human-like partner—i.
As we have previously discussed, roles naturally develop during physical human—human interaction [ 38 — 40 ]. These roles can be generally classified as an active leader role and a passive follower role [ 38 , 40 ], where both human participants can dynamically take and switch between the complementary roles.
Extending these experimental results into human—robot interaction, researchers such as Li et al. Furthermore, at times when the human and robot disagree, the robot should yield control back to the human, and quickly transition into a follower role. Consider, for instance, the work conducted by Evrard and Kheddar [ 62 ]; these authors provide a simple mathematical framework for the robot to interpolate between leader and follower roles.
The robot simultaneously maintains a leader controller, which minimizes errors from the desired trajectory high-impedance , and a follower controller, which reduces the forces felt by the human zero-impedance. When dividing forces within the redundant voluntary degree-of-freedom, the human can provide more of the voluntary effort, making the robot a follower, or the robot can perform more of the voluntary effort, thereby taking a leader role. We might wonder, however, how the robot should behave when this correct arbitration between collaborative leader and follower roles is not externally provided.
Works by Medina et al. When the robot is confident that the human will behave a certain way—for instance, follow a known trajectory [ 63 , 65 ]—the robot assumes a leader role in order to reduce the human's effort. On the other hand, when the robot is confident that the human wants to deviate from the robot's current trajectory—i. Although the leader and follower roles appear to be the most prevalent form of collaborative arbitration, research by Kucukyilmaz et al. Here, the human takes the leader role during larger scale motions, which direct the robot toward its goal pose, while the robot takes the leader role during finer, smaller scale motions, i.
This form of collaborative arbitration, which was also found in human—human dyads [ 39 ], is somewhat akin to co-activity, where the human's subtask might entail larger, less constrained movements, and the robot is tasked with the smaller, intricate motions. In the preceding discussion of the different types of role arbitration, we have already seen indications that role arbitrations can dynamically change during task execution.
These changes could occur within the same type of role allocation—such as switching between leader and follower roles during collaborative arbitration—or between two different types of role allocation—such as gradually transitioning between master—slave and single-agent roles during teacher—student arbitration. In general, however, dynamic changes in role arbitration are meant to either increase the robot's level of autonomy at the expense of the human's authority, or, conversely, increase the human's control over the shared cooperative activity at the expense of the robot's autonomy.
Referring back to Fig. In what follows, we will outline the two predominant tools employed within the shared control literature to determine when to change arbitration: machine learning and performance metrics. These data-driven approaches typically require a supervised training phase, where the human practices communicating intents with known classifications; after the model is trained, it can be applied to accurately change role arbitrations in real time. Works by Li and Okamura [ 53 ], Yu et al. For instance, in Ref. Similarly, in Ref. Another interesting approach for adjusting arbitration using HMMs was recently proposed by Kulic and Croft [ 68 ].
In their research, the HMM attempts to estimate the human's affective emotional state from measured physiological signals including heart rate, skin conductance, and facial muscle contractions. First, the human and robot are placed in close proximity, and the human's affective state is estimated in response to the behavior of the robotic manipulator. Indeed, a meta-analysis by Hancock et al. It seems reasonable, therefore, to employ performance metrics as a means to dynamically change role arbitrations between human and robot. A straightforward performance metric for this purpose could simply be the amount of force or torque applied by the human; both Kucukyilmaz et al.
In essence, when the human applies larger efforts, these authors argue that the human is actively attempting to take control of the task, and hence arbitration should shift toward the human. Conversely, when the human is passive, and not significantly interacting with the robot, arbitration switches to grant the robot a larger portion of the shared control.
A related performance metric was developed by Thobbi et al. Alternatively, when the human interacts with the robot in a manner at odds with the robot's internal predictions, the robot returns control to the human and begins to develop new predictive models. More generally, we can imagine that the human has a reward function, which the robot can learn from human—robot interactions [ 66 , 70 , 71 ]. As before, the robot lets the human take control during interactions, and then resumes autonomous behavior after the human stops interacting.
Next, based on how the human interacted, the robot updates its estimate of the human's reward function—i. Dragan and Srinivasa [ 66 ] have applied this concept to robotic teleoperation systems which are unsure of the human's goal position: when the human inputs new commands into the teleoperation interface, the robot updates its estimate of the desired goal.
Once the robot is quite confident that the human is trying to reach a particular goal, then the robot becomes more dominant in the shared control; when the robot is unsure, however, the human moves with little robotic assistance. Works by Losey and O'Malley [ 70 ] and Bajcsy et al. At the other end of the spectrum, we can also use measured outcomes from the previous task to update role allocations for the next task—in Pehlivan et al.
Updating between trials is best suited for applications where the human and robot will be performing the same task for multiple iterations [ 56 ], just like the optimization approach proposed by Medina et al. Within their work, Medina et al. The human is recorded performing the task multiple times, and, by incorporating the Mahalanobis distance, arbitration shifts to favor the human over portions of the trajectory where large motion uncertainty is present: for instance, should the robot and human go around an obstacle in a clockwise or counterclockwise direction?
Next, risk-sensitive optimal control is introduced to determine how the robot will respond to conflicts with the human: should the robot yield leadership, or take a more aggressive, dominant role? To conclude, we summarize the discussed performance metrics [ 10 , 28 , 63 — 67 ] as fundamentally derived from the concept of trust between human and robot. As the human comes to trust that the robot will behave as expected, and, simultaneously, as the robot better learns what the human wants to accomplish, arbitration can dynamically change to increase the robot's level of shared control.
Once a type of role arbitration has been determined, and the current role allocation is decided, the robot can provide feedback to the user based on both the environment and this arbitration. From this feedback, the user can better infer the current arbitration strategy, and understand their role within the interaction. When a human and robot are physically coupled and sharing control of a task, such as an amputee using an advanced prosthetic device, the user depends on the robotic system to not only replace the function of the missing limb, detect their intent, and arbitrate control of the task, but also to communicate back to the human operator the properties of the environment.
A similar situation occurs in bilateral telemanipulation, where force cues that arise between the remote tool and environment are relayed to the human operator at the master manipulator. In applications where a robotic device held or worn by the human operator is intended to instruct or assist with task performance, such as would be the case for surgical simulators, motor learning platforms, and exoskeletons for gait rehabilitation or upper limb reaching, to name a few, it is necessary to convey not just task or environment forces either real or virtual, but also the desired actions and behaviors that the human should execute.
Further still, one can picture scenarios where the human user should be informed of the intent or future actions of the robot. In this section, examples of the methods employed by robotic systems to communicate with the human operator in shared control scenarios are surveyed using example applications. The communication mechanism between human and robot in a coupled shared control system typically relies on the sensory channels available for information conveyance.
For example, feedback can be provided visually, aurally, or haptically. For applications of physical human—robot interaction, the haptic channel is of particular interest because the force—motion coupling between action on the environment and resultant forces and actions can be leveraged in much the same way that our own body uses sensors embedded in the muscles to modulate the forces that we impose on the environment.
A depiction of these different types of sensory feedback can be seen in Fig. Haptic feedback, which is a general reference to cues that are referred via our sense of touch, can be subdivided as kinesthetic feedback forces and torque applied to the human body and sensed at the muscles and joints , and cutaneous or tactile forces and sensations sensed through the mechanoreceptors in our skin. Kinesthetic feedback requires complex, custom haptic devices unique to a particular task to be trained for example, multi-degree-of-freedom devices to simulate rowing [ 72 , 73 ] or tennis swings [ 74 ].
Some devices are used to convey forces to the upper limb moving on a planar working surface [ 75 ] using an end effector-based design. ZBA chair Christine Araujo referred the matter to the city law department. He said the hearing was brief; Vincent Morgan, DMD, president of Bicon [which has a staff of 15 dentists, technicians and hygienists] was there but didn't speak, and about 20 Yale Terrace residents attended. O'Connor and Yale Terrace residents were appealing a Dec 3, decision by the Inspectional Services Department's attorney denying their request that Bicon apply for an occupancy permit.
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