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- Creators: Arizona State University
- Creators: Software Engineering
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 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
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 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
Description
Bicycles are already used for daily transportation by a large share of the world's population and provide a partial solution for many issues facing the world today. The low environmental impact of bicycling combined with the reduced requirement for road and parking spaces makes bicycles a good choice for transportation over short distances in urban areas. Bicycle riding has also been shown to improve overall health and increase life expectancy. However, riding a bicycle may be inconvenient or impossible for persons with disabilities due to the complex and coordinated nature of the task. Automated bicycles provide an interesting area of study for human-robot interaction, due to the number of contact points between the rider and the bicycle. The goal of the Smart Bike project is to provide a platform for future study of the physical interaction between a semi-autonomous bicycle robot and a human rider, with possible applications in rehabilitation and autonomous vehicle research.
This thesis presents the development of two balance control systems, which utilize actively controlled steering and a control moment gyroscope to stabilize the bicycle at high and low speeds. These systems may also be used to introduce disturbances, which can be useful for studying human reactions. The effectiveness of the steering balance control system is verified through testing with a PID controller in an outdoor environment. Also presented is the development of a force sensitive bicycle seat which provides feedback used to estimate the pose of the rider on the bicycle. The relationship between seat force distribution is demonstrated with a motion capture experiment. A corresponding software system is developed for balance control and sensor integration, with inputs from the rider, the internal balance and steering controller, and a remote operator.
This thesis presents the development of two balance control systems, which utilize actively controlled steering and a control moment gyroscope to stabilize the bicycle at high and low speeds. These systems may also be used to introduce disturbances, which can be useful for studying human reactions. The effectiveness of the steering balance control system is verified through testing with a PID controller in an outdoor environment. Also presented is the development of a force sensitive bicycle seat which provides feedback used to estimate the pose of the rider on the bicycle. The relationship between seat force distribution is demonstrated with a motion capture experiment. A corresponding software system is developed for balance control and sensor integration, with inputs from the rider, the internal balance and steering controller, and a remote operator.
ContributorsBush, Jonathan Ernest (Author) / Zhang, Wenlong (Thesis director) / Sandy, Douglas (Committee member) / Software Engineering (Contributor, Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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 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
Description
Many organizational course design methodologies feature general guidelines for the chronological and time-management aspects of course design development. Proper course structure and instructional strategy pacing has been shown to facilitate student knowledge acquisition of novel material. These course-scheduling details influencing student learning outcomes implies the need for an effective and tightly coupled component of an instructional module. The Instructional Module Development System, or IMODS, seeks to improve STEM, or ‘science, technology, engineering, and math’, education, by equipping educators with a powerful informational tool that helps guide course design by providing information based on contemporary research about pedagogical methodology and assessment practices. This is particularly salient within the higher-education STEM fields because many instructors come from backgrounds that are more technical and most Ph.Ds. in science fields have traditionally not focused on preparing doctoral candidates to teach. This thesis project aims to apply a multidisciplinary approach, blending educational psychology and computer science, to help improve STEM education. By developing an instructional module-scheduling feature for the Web-based IMODS, Instructional Module Development System, system, we can help instructors plan out and organize their course work inside and outside of the classroom, while providing them with relevant helpful research that will help them improve their courses. This article illustrates the iterative design process to gather background research on pacing of workload and learning activities and their influence on student knowledge acquisition, constructively critique and analyze pre-existing information technology (IT) scheduling tools, synthesize graphical user interface, or GUI, mockups based on the background research, and then implement a functional-working prototype using the IMODs framework.
ContributorsCoomber, Wesley Poblete (Author) / Bansal, Srividya (Thesis director) / Lindquist, Timothy (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the
burden on society. It will be essential to develop technology that can age with the
individual. One solution is to keep older adults in their homes longer through smart
home and smart living technology, allowing them to age in place. People have many
choices when choosing where to age in place, including their own homes, assisted
living facilities, nursing homes, or family members. No matter where people choose to
age, they may face isolation and financial hardships. It is crucial to keep finances in
mind when developing Smart Home technology.
Smart home technologies seek to allow individuals to stay inside their homes for
as long as possible, yet little work looks at how we can use technology in different
life stages. Robots are poised to impact society and ease burns at home and in the
workforce. Special attention has been given to social robots to ease isolation. As
social robots become accepted into society, researchers need to understand how these
robots should mimic natural conversation. My work attempts to answer this question
within social robotics by investigating how to make conversational robots natural and
reciprocal.
I investigated this through a 2x2 Wizard of Oz between-subjects user study. The
study lasted four months, testing four different levels of interactivity with the robot.
None of the levels were significantly different from the others, an unexpected result. I
then investigated the robot’s personality, the participant’s trust, and the participant’s
acceptance of the robot and how that influenced the study.
ContributorsMiller, Jordan (Author) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Cooke, Nancy (Committee member) / Bryan, Chris (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Enabling robots to physically engage with their environment in a safe and efficient manner is an essential step towards human-robot interaction. To date, robots usually operate as pre-programmed workers that blindly execute tasks in highly structured environments crafted by skilled engineers. Changing the robots’ behavior to cover new duties or handle variability is an expensive, complex, and time-consuming process. However, with the advent of more complex sensors and algorithms, overcoming these limitations becomes within reach. This work proposes innovations in artificial intelligence, language understanding, and multimodal integration to enable next-generation grasping and manipulation capabilities in autonomous robots. The underlying thesis is that multimodal observations and instructions can drastically expand the responsiveness and dexterity of robot manipulators. Natural language, in particular, can be used to enable intuitive, bidirectional communication between a human user and the machine. To this end, this work presents a system that learns context-aware robot control policies from multimodal human demonstrations. Among the main contributions presented are techniques for (a) collecting demonstrations in an efficient and intuitive fashion, (b) methods for leveraging physical contact with the environment and objects, (c) the incorporation of natural language to understand context, and (d) the generation of robust robot control policies. The presented approach and systems are evaluated in multiple grasping and manipulation settings ranging from dexterous manipulation to pick-and-place, as well as contact-rich bimanual insertion tasks. Moreover, the usability of these innovations, especially when utilizing human task demonstrations and communication interfaces, is evaluated in several human-subject studies.
ContributorsStepputtis, Simon (Author) / Ben Amor, Heni (Thesis advisor) / Baral, Chitta (Committee member) / Yang, Yezhou (Committee member) / Lee, Stefan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The rapid increase in the volume and complexity of data lead to accelerated Artificial Intelligence (AI) applications, primarily as intelligent machines, in everyday life. Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework, which provides the rationale behind an AI agent's decision-making. Therefore, the validity of the AI models is constrained based on their ability to explain their decision-making rationale. On the other hand, AI agents cannot perceive the social situation that human experts may recognize using their background knowledge, specifically in cybersecurity and the military. Social behavior depends on situation awareness, and it relies on interpretability, transparency, and fairness when we envision efficient Human-AI collaboration. Consequently, the human remains an essential element for planning, especially when the problem's constraints are difficult to express for an agent in a dynamic setting.
This dissertation will first develop different model-based explanation generation approaches to predict where the human teammate would misunderstand the plan and, therefore, generate an explanation accordingly. The robot's generated explanation or interactive explicable behavior maintains the human teammate's cognitive workload and increases the overall team situation awareness throughout human-robot interaction. Further, it will focus on a rule-based model to preserve the collaborative engagement of the team by exploring essential aspects of the facilitator agent design. In addition to recognizing wherein the plan might be discrepancies, focusing on the decision-making process provides insight into the reason behind the conflict between the human expectation and the robot's behavior. Employing a rule-based framework will shift the focus from assisting an individual (human) teammate to helping the team interactively while maintaining collaboration. Hence, concentrating on teaming provides the opportunity to recognize some cognitive biases that skew the teammate's expectations and affect interaction behavior.
This dissertation investigates how to maintain collaboration engagement or cognitive readiness for collaborative planning tasks. Moreover, this dissertation aims to lay out a planning framework focusing on the human teammate's cognitive abilities to understand the machine-provided explanations while collaborating on a planning task. Consequently, this dissertation explored the design for AI facilitator, helping a team tasked with a challenging task to plan collaboratively, mitigating the teaming biases, and communicate effectively. This dissertation investigates the effect of some cognitive biases on the task outcome and shapes the utility function. The facilitator's role is to facilitate goal alignment, the consensus of planning strategies, utility management, effective communication, and mitigate biases.
ContributorsZakershahrak, Mehrdad (Author) / Cooke, Nancy NC (Thesis advisor) / Zhang, Yu YZ (Thesis advisor) / Ben Amor, Hani HB (Committee member) / Srivastava, Siddharth SS (Committee member) / Hsiao, Sharon SH (Committee member) / Arizona State University (Publisher)
Created2021
Description
Imitation learning is a promising methodology for teaching robots how to physically interact and collaborate with human partners. However, successful interaction requires complex coordination in time and space, i.e., knowing what to do as well as when to do it. This dissertation introduces Bayesian Interaction Primitives, a probabilistic imitation learning framework which establishes a conceptual and theoretical relationship between human-robot interaction (HRI) and simultaneous localization and mapping. In particular, it is established that HRI can be viewed through the lens of recursive filtering in time and space. In turn, this relationship allows one to leverage techniques from an existing, mature field and develop a powerful new formulation which enables multimodal spatiotemporal inference in collaborative settings involving two or more agents. Through the development of exact and approximate variations of this method, it is shown in this work that it is possible to learn complex real-world interactions in a wide variety of settings, including tasks such as handshaking, cooperative manipulation, catching, hugging, and more.
ContributorsCampbell, Joseph (Author) / Ben Amor, Heni (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Yamane, Katsu (Committee member) / Kambhampati, Subbarao (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation focuses on reinforcement learning (RL) controller design aiming for real-life applications in continuous state and control problems. It involves three major research investigations in the aspect of design, analysis, implementation, and evaluation. The application case addresses automatically configuring robotic prosthesis impedance parameters. Major contributions of the dissertation include the following. 1) An “echo control” using the intact knee profile as target is designed to overcome the limitation of a designer prescribed robotic knee profile. 2) Collaborative multiagent reinforcement learning (cMARL) is proposed to directly take into account human influence in the robot control design. 3) A phased actor in actor-critic (PAAC) reinforcement learning method is developed to reduce learning variance in RL. The design of an “echo control” is based on a new formulation of direct heuristic dynamic programming (dHDP) for tracking control of a robotic knee prosthesis to mimic the intact knee profile. A systematic simulation of the proposed control is provided using a human-robot system simulation in OpenSim. The tracking controller is then tested on able-bodied and amputee subjects. This is the first real-time human testing of RL tracking control of a robotic knee to mirror the profile of an intact knee. The cMARL is a new solution framework for the human-prosthesis collaboration (HPC) problem. This is the first attempt at considering human influence on human-robot walking with the presence of a reinforcement learning controlled lower limb prosthesis. Results show that treating the human and robot as coupled and collaborating agents and using an estimated human adaptation in robot control design help improve human walking performance. The above studies have demonstrated great potential of RL control in solving continuous problems. To solve more complex real-life tasks with multiple control inputs and high dimensional state space, high variance, low data efficiency, slow learning or even instability are major roadblocks to be addressed. A novel PAAC method is proposed to improve learning performance in policy gradient RL by accounting for both Q value and TD error in actor updates. Systematical and comprehensive demonstrations show its effectiveness by qualitative analysis and quantitative evaluation in DeepMind Control Suite.
ContributorsWu, Ruofan (Author) / Si, Jennie (Thesis advisor) / Huang, He (Committee member) / Santello, Marco (Committee member) / Papandreou- Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2023