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Description
As robotic systems are used in increasingly diverse applications, the interaction of humans and robots has become an important area of research. In many of the applications of physical human robot interaction (pHRI), the robot and the human can be seen as cooperating to complete a task with some object

As robotic systems are used in increasingly diverse applications, the interaction of humans and robots has become an important area of research. In many of the applications of physical human robot interaction (pHRI), the robot and the human can be seen as cooperating to complete a task with some object of interest. Often these applications are in unstructured environments where many paths can accomplish the goal. This creates a need for the ability to communicate a preferred direction of motion between both participants in order to move in coordinated way. This communication method should be bidirectional to be able to fully utilize both the robot and human capabilities. Moreover, often in cooperative tasks between two humans, one human will operate as the leader of the task and the other as the follower. These roles may switch during the task as needed. The need for communication extends into this area of leader-follower switching. Furthermore, not only is there a need to communicate the desire to switch roles but also to control this switching process. Impedance control has been used as a way of dealing with some of the complexities of pHRI. For this investigation, it was examined if impedance control can be utilized as a way of communicating a preferred direction between humans and robots. The first set of experiments tested to see if a human could detect a preferred direction of a robot by grasping and moving an object coupled to the robot. The second set tested the reverse case if the robot could detect the preferred direction of the human. The ability to detect the preferred direction was shown to be up to 99% effective. Using these results, a control method to allow a human and robot to switch leader and follower roles during a cooperative task was implemented and tested. This method proved successful 84% of the time. This control method was refined using adaptive control resulting in lower interaction forces and a success rate of 95%.
ContributorsWhitsell, Bryan (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Arizona State University (Publisher)
Created2014
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Description
As robots are increasingly migrating out of factories and research laboratories and into our everyday lives, they should move and act in environments designed for humans. For this reason, the need of anthropomorphic movements is of utmost importance. The objective of this thesis is to solve the inverse kinematics problem

As robots are increasingly migrating out of factories and research laboratories and into our everyday lives, they should move and act in environments designed for humans. For this reason, the need of anthropomorphic movements is of utmost importance. The objective of this thesis is to solve the inverse kinematics problem of redundant robot arms that results to anthropomorphic configurations. The swivel angle of the elbow was used as a human arm motion parameter for the robot arm to mimic. The swivel angle is defined as the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Using kinematic data recorded from human subjects during every-day life tasks, the linear sensorimotor transformation model was validated and used to estimate the swivel angle, given the desired end-effector position. Defining the desired swivel angle simplifies the kinematic redundancy of the robot arm. The proposed method was tested with an anthropomorphic redundant robot arm and the computed motion profiles were compared to the ones of the human subjects. This thesis shows that the method computes anthropomorphic configurations for the robot arm, even if the robot arm has different link lengths than the human arm and starts its motion at random configurations.
ContributorsWang, Yuting (Author) / Artemiadis, Panagiotis (Thesis advisor) / Mignolet, Marc (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.

Humans desire compliant robots to safely interact in dynamic environments

associated with daily activities. As surface electromyography non-invasively measures

limb motion intent and correlates with joint stiness during co-contractions,

it has been identied as a candidate for naturally controlling such robots. However,

state-of-the-art myoelectric

Myoelectric control is lled with potential to signicantly change human-robot interaction.

Humans desire compliant robots to safely interact in dynamic environments

associated with daily activities. As surface electromyography non-invasively measures

limb motion intent and correlates with joint stiness during co-contractions,

it has been identied as a candidate for naturally controlling such robots. However,

state-of-the-art myoelectric interfaces have struggled to achieve both enhanced

functionality and long-term reliability. As demands in myoelectric interfaces trend

toward simultaneous and proportional control of compliant robots, robust processing

of multi-muscle coordinations, or synergies, plays a larger role in the success of the

control scheme. This dissertation presents a framework enhancing the utility of myoelectric

interfaces by exploiting motor skill learning and

exible muscle synergies for

reliable long-term simultaneous and proportional control of multifunctional compliant

robots. The interface is learned as a new motor skill specic to the controller,

providing long-term performance enhancements without requiring any retraining or

recalibration of the system. Moreover, the framework oers control of both motion

and stiness simultaneously for intuitive and compliant human-robot interaction. The

framework is validated through a series of experiments characterizing motor learning

properties and demonstrating control capabilities not seen previously in the literature.

The results validate the approach as a viable option to remove the trade-o

between functionality and reliability that have hindered state-of-the-art myoelectric

interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric

controlled applications beyond commonly perceived anthropomorphic and

\intuitive control" constraints and into more advanced robotic systems designed for

everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
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Description
As robotic technology and its various uses grow steadily more complex and ubiquitous, humans are coming into increasing contact with robotic agents. A large portion of such contact is cooperative interaction, where both humans and robots are required to work on the same application towards achieving common goals. These application

As robotic technology and its various uses grow steadily more complex and ubiquitous, humans are coming into increasing contact with robotic agents. A large portion of such contact is cooperative interaction, where both humans and robots are required to work on the same application towards achieving common goals. These application scenarios are characterized by a need to leverage the strengths of each agent as part of a unified team to reach those common goals. To ensure that the robotic agent is truly a contributing team-member, it must exhibit some degree of autonomy in achieving goals that have been delegated to it. Indeed, a significant portion of the utility of such human-robot teams derives from the delegation of goals to the robot, and autonomy on the part of the robot in achieving those goals. In order to be considered truly autonomous, the robot must be able to make its own plans to achieve the goals assigned to it, with only minimal direction and assistance from the human.

Automated planning provides the solution to this problem -- indeed, one of the main motivations that underpinned the beginnings of the field of automated planning was to provide planning support for Shakey the robot with the STRIPS system. For long, however, automated planners suffered from scalability issues that precluded their application to real world, real time robotic systems. Recent decades have seen a gradual abeyance of those issues, and fast planning systems are now the norm rather than the exception. However, some of these advances in speedup and scalability have been achieved by ignoring or abstracting out challenges that real world integrated robotic systems must confront.

In this work, the problem of planning for human-hobot teaming is introduced. The central idea -- the use of automated planning systems as mediators in such human-robot teaming scenarios -- and the main challenges inspired from real world scenarios that must be addressed in order to make such planning seamless are presented: (i) Goals which can be specified or changed at execution time, after the planning process has completed; (ii) Worlds and scenarios where the state changes dynamically while a previous plan is executing; (iii) Models that are incomplete and can be changed during execution; and (iv) Information about the human agent's plan and intentions that can be used for coordination. These challenges are compounded by the fact that the human-robot team must execute in an open world, rife with dynamic events and other agents; and in a manner that encourages the exchange of information between the human and the robot. As an answer to these challenges, implemented solutions and a fielded prototype that combines all of those solutions into one planning system are discussed. Results from running this prototype in real world scenarios are presented, and extensions to some of the solutions are offered as appropriate.
ContributorsTalamadupula, Kartik (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Liu, Huan (Committee member) / Scheutz, Matthias (Committee member) / Smith, David E. (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Human running requires extensive training and conditioning for an individual to maintain high speeds (greater than 10mph) for an extended duration of time. Studies have shown that running at peak speeds generates a high metabolic cost due to the use of large muscle groups in the legs associated with

Human running requires extensive training and conditioning for an individual to maintain high speeds (greater than 10mph) for an extended duration of time. Studies have shown that running at peak speeds generates a high metabolic cost due to the use of large muscle groups in the legs associated with the human gait cycle. Applying supplemental external and internal forces to the human body during the gait cycle has been shown to decrease the metabolic cost for walking, allowing individuals to carry additional weight and walk further distances. Significant research has been conducted to reduce the metabolic cost of walking, however, there are few if any documented studies that focus specifically on reducing the metabolic cost associated with high speed running. Three mechanical systems were designed to work in concert with the human user to decrease metabolic cost and increase the range and speeds at which a human can run.

The methods of design require a focus on mathematical modeling, simulations, and metabolic cost. Mathematical modeling and simulations are used to aid in the design process of robotic systems and metabolic testing is regarded as the final analysis process to determine the true effectiveness of robotic prototypes. Metabolic data, (VO2) is the volumetric consumption of oxygen, per minute, per unit mass (ml/min/kg). Metabolic testing consists of analyzing the oxygen consumption of a test subject while performing a task naturally and then comparing that data with analyzed oxygen consumption of the same task while using an assistive device.

Three devices were designed and tested to augment high speed running. The first device, AirLegs V1, is a mostly aluminum exoskeleton with two pneumatic linear actuators connecting from the lower back directly to the user's thighs, allowing the device to induce a torque on the leg by pushing and pulling on the user's thigh during running. The device also makes use of two smaller pneumatic linear actuators which drive cables connecting to small lever arms at the back of the heel, inducing a torque at the ankles. Device two, AirLegs V2, is also pneumatically powered but is considered to be a soft suit version of the first device. It uses cables to interface the forces created by actuators located vertically on the user's back. These cables then connect to the back of the user's knees resulting in greater flexibility and range of motion of the legs. Device three, a Jet Pack, produces an external force against the user's torso to propel a user forward and upward making it easier to run. Third party testing, pilot demonstrations and timed trials have demonstrated that all three of the devices effectively reduce the metabolic cost of running below that of natural running with no device.
ContributorsKerestes, Jason (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2014
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Description
While developing autonomous intelligent robots has been the goal of many research programs, a more practical application involving intelligent robots is the formation of teams consisting of both humans and robots. An example of such an application is search and rescue operations where robots commanded by humans are sent to

While developing autonomous intelligent robots has been the goal of many research programs, a more practical application involving intelligent robots is the formation of teams consisting of both humans and robots. An example of such an application is search and rescue operations where robots commanded by humans are sent to environments too dangerous for humans. For such human-robot interaction, natural language is considered a good communication medium as it allows humans with less training about the robot's internal language to be able to command and interact with the robot. However, any natural language communication from the human needs to be translated to a formal language that the robot can understand. Similarly, before the robot can communicate (in natural language) with the human, it needs to formulate its communique in some formal language which then gets translated into natural language. In this paper, I develop a high level language for communication between humans and robots and demonstrate various aspects through a robotics simulation. These language constructs borrow some ideas from action execution languages and are grounded with respect to simulated human-robot interaction transcripts.
ContributorsLumpkin, Barry Thomas (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Humans and robots need to work together as a team to accomplish certain shared goals due to the limitations of current robot capabilities. Human assistance is required to accomplish the tasks as human capabilities are often better suited for certain tasks and they complement robot capabilities in many situations. Given

Humans and robots need to work together as a team to accomplish certain shared goals due to the limitations of current robot capabilities. Human assistance is required to accomplish the tasks as human capabilities are often better suited for certain tasks and they complement robot capabilities in many situations. Given the necessity of human-robot teams, it has been long assumed that for the robotic agent to be an effective team member, it must be equipped with automated planning technologies that helps in achieving the goals that have been delegated to it by their human teammates as well as in deducing its own goal to proactively support its human counterpart by inferring their goals. However there has not been any systematic evaluation on the accuracy of this claim.

In my thesis, I perform human factors analysis on effectiveness of such automated planning technologies for remote human-robot teaming. In the first part of my study, I perform an investigation on effectiveness of automated planning in remote human-robot teaming scenarios. In the second part of my study, I perform an investigation on effectiveness of a proactive robot assistant in remote human-robot teaming scenarios.

Both investigations are conducted in a simulated urban search and rescue (USAR) scenario where the human-robot teams are deployed during early phases of an emergency response to explore all areas of the disaster scene. I evaluate through both the studies, how effective is automated planning technology in helping the human-robot teams move closer to human-human teams. I utilize both objective measures (like accuracy and time spent on primary and secondary tasks, Robot Attention Demand, etc.) and a set of subjective Likert-scale questions (on situation awareness, immediacy etc.) to investigate the trade-offs between different types of remote human-robot teams. The results from both the studies seem to suggest that intelligent robots with automated planning capability and proactive support ability is welcomed in general.
ContributorsNarayanan, Vignesh (Author) / Kambhampati, Subbarao (Thesis advisor) / Zhang, Yu (Thesis advisor) / Cooke, Nancy J. (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
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Description
In a collaborative environment where multiple robots and human beings are expected

to collaborate to perform a task, it becomes essential for a robot to be aware of multiple

agents working in its work environment. A robot must also learn to adapt to

different agents in the workspace and conduct its interaction based

In a collaborative environment where multiple robots and human beings are expected

to collaborate to perform a task, it becomes essential for a robot to be aware of multiple

agents working in its work environment. A robot must also learn to adapt to

different agents in the workspace and conduct its interaction based on the presence

of these agents. A theoretical framework was introduced which performs interaction

learning from demonstrations in a two-agent work environment, and it is called

Interaction Primitives.

This document is an in-depth description of the new state of the art Python

Framework for Interaction Primitives between two agents in a single as well as multiple

task work environment and extension of the original framework in a work environment

with multiple agents doing a single task. The original theory of Interaction

Primitives has been extended to create a framework which will capture correlation

between more than two agents while performing a single task. The new state of the

art Python framework is an intuitive, generic, easy to install and easy to use python

library which can be applied to use the Interaction Primitives framework in a work

environment. This library was tested in simulated environments and controlled laboratory

environment. The results and benchmarks of this library are available in the

related sections of this document.
ContributorsKumar, Ashish, M.S (Author) / Amor, Hani Ben (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
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Description
What if there is a way to integrate prosthetics seamlessly with the human body and robots could help improve the lives of children with disabilities? With physical human-robot interaction being seen in multiple aspects of life, including industry, medical, and social, how these robots are interacting with human becomes

What if there is a way to integrate prosthetics seamlessly with the human body and robots could help improve the lives of children with disabilities? With physical human-robot interaction being seen in multiple aspects of life, including industry, medical, and social, how these robots are interacting with human becomes even more important. Therefore, how smoothly the robot can interact with a person will determine how safe and efficient this relationship will be. This thesis investigates adaptive control method that allows a robot to adapt to the human's actions based on the interaction force. Allowing the relationship to become more effortless and less strained when the robot has a different goal than the human, as seen in Game Theory, using multiple techniques that adapts the system. Few applications this could be used for include robots in physical therapy, manufacturing robots that can adapt to a changing environment, and robots teaching people something new like dancing or learning how to walk after surgery.

The experience gained is the understanding of how a cost function of a system works, including the tracking error, speed of the system, the robot’s effort, and the human’s effort. Also, this two-agent system, results into a two-agent adaptive impedance model with an input for each agent of the system. This leads to a nontraditional linear quadratic regulator (LQR), that must be separated and then added together. Thus, creating a traditional LQR. This new experience can be used in the future to help build better safety protocols on manufacturing robots. In the future the knowledge learned from this research could be used to develop technologies for a robot to allow to adapt to help counteract human error.
ContributorsBell, Rebecca C (Author) / Zhang, Wenlong (Thesis advisor) / Chiou, Erin (Committee member) / Aukes, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
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Description
As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks.  For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task.  This research project aims to

As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks.  For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task.  This research project aims to investigate human-robot trust through a collaborative game of logic that can be played with a human and a robot together. This thesis details the development of a game of logic that could be used for this purpose. The game of logic is based upon a popular game in AI research called ‘Wumpus World’. The original Wumpus World game was a low-interactivity game to be played by humans alone. In this project, the Wumpus World game is modified for a high degree of interactivity with a human player, while also allowing the game to be played simultaneously by an AI algorithm.
ContributorsBoateng, Andrew Owusu (Author) / Sodemann, Angela (Thesis director) / Martin, Thomas (Committee member) / Software Engineering (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05