Matching Items (39)
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 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.
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
Description
Robots are becoming an important part of our life and industry. Although a lot of robot control interfaces have been developed to simplify the control method and improve user experience, users still cannot control robots comfortably. With the improvements of the robot functions, the requirements of universality and ease of use of robot control interfaces are also increasing. This research introduces a graphical interface for Linear Temporal Logic (LTL) specifications for mobile robots. It is a sketch based interface built on the Android platform which makes the LTL control interface more friendly to non-expert users. By predefining a set of areas of interest, this interface can quickly and efficiently create plans that satisfy extended plan goals in LTL. The interface can also allow users to customize the paths for this plan by sketching a set of reference trajectories. Given the custom paths by the user, the LTL specification and the environment, the interface generates a plan balancing the customized paths and the LTL specifications. We also show experimental results with the implemented interface.
ContributorsWei, Wei (Author) / Fainekos, Georgios (Thesis advisor) / Amor, Hani Ben (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2016
Description
Sports activities have been a cornerstone in the evolution of humankind through the ages from the ancient Roman empire to the Olympics in the 21st century. These activities have been used as a benchmark to evaluate the how humans have progressed through the sands of time. In the 21st century, machines along with the help of powerful computing and relatively new computing paradigms have made a good case for taking up the mantle. Even though machines have been able to perform complex tasks and maneuvers, they have struggled to match the dexterity, coordination, manipulability and acuteness displayed by humans. Bi-manual tasks are more complex and bring in additional variables like coordination into the task making it harder to evaluate.
A task capable of demonstrating the above skillset would be a good measure of the progress in the field of robotic technology. Therefore a dual armed robot has been built and taught to handle the ball and make the basket successfully thus demonstrating the capability of using both arms. A combination of machine learning techniques, Reinforcement learning, and Imitation learning has been used along with advanced optimization algorithms to accomplish the task.
A task capable of demonstrating the above skillset would be a good measure of the progress in the field of robotic technology. Therefore a dual armed robot has been built and taught to handle the ball and make the basket successfully thus demonstrating the capability of using both arms. A combination of machine learning techniques, Reinforcement learning, and Imitation learning has been used along with advanced optimization algorithms to accomplish the task.
ContributorsKalige, Nikhil (Author) / Amor, Heni Ben (Thesis advisor) / Shrivastava, Aviral (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2016
Description
Automated planning addresses the problem of generating a sequence of actions that enable a set of agents to achieve their goals.This work investigates two important topics from the field of automated planning, namely model-lite planning and multi-agent planning. For model-lite planning, I focus on a prominent model named Annotated PDDL and it's related application of robust planning. For this model, I try to identify a method of leveraging additional domain information (available in the form of successful plan traces). I use this information to refine the set of possible domains to generate more robust plans (as compared to the original planner) for any given problem. This method also provides us a way of overcoming one of the major drawbacks of the original approach, namely the need for a domain writer to explicitly identify the annotations.
For the second topic, the central question I ask is ``{\em under what conditions are multiple agents actually needed to solve a given planning problem?}''. To answer this question, the multi-agent planning (MAP) problem is classified into several sub-classes and I identify the conditions in each of these sub-classes that can lead to required cooperation (RC). I also identify certain sub-classes of multi-agent planning problems (named DVC-RC problems), where the problems can be simplified using a single virtual agent. This insight is later used to propose a new planner designed to solve problems from these subclasses. Evaluation of this new planner on all the current multi-agent planning benchmarks reveals that most current multi-agent planning benchmarks only belong to a small subset of possible classes of multi-agent planning problems.
For the second topic, the central question I ask is ``{\em under what conditions are multiple agents actually needed to solve a given planning problem?}''. To answer this question, the multi-agent planning (MAP) problem is classified into several sub-classes and I identify the conditions in each of these sub-classes that can lead to required cooperation (RC). I also identify certain sub-classes of multi-agent planning problems (named DVC-RC problems), where the problems can be simplified using a single virtual agent. This insight is later used to propose a new planner designed to solve problems from these subclasses. Evaluation of this new planner on all the current multi-agent planning benchmarks reveals that most current multi-agent planning benchmarks only belong to a small subset of possible classes of multi-agent planning problems.
ContributorsSreedharan, Sarath (Author) / Kambhampati, Subbarao (Thesis advisor) / Zhang, Yu (Thesis advisor) / Ben Amor, Heni (Committee member) / Arizona State University (Publisher)
Created2016
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 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.
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
Description
Research literature was reviewed to find recommended tools and technologies for operating Unmanned Aerial Systems (UAS) fleets in an urban environment. However, restrictive legislation prohibits fully autonomous flight without an operator. Existing literature covers considerations for operating UAS fleets in a controlled environment, with an emphasis on the effect different networking approaches have on the topology of the UAS network. The primary network topology used to implement UAS communications is 802.11 protocols, which can transmit telemetry and a video stream using off the shelf hardware. Other implementations use low-frequency radios for long distance communication, or higher latency 4G LTE modems to access existing network infrastructure. However, a gap remains testing different network topologies outside of a controlled environment.
With the correct permits in place, further research can explore how different UAS network topologies behave in an urban environment when implemented with off the shelf UAS hardware. In addition to testing different network topologies, this thesis covers the implementation of building a secure, scalable system using modern cloud computation tools and services capable of supporting a variable number of UAS. The system also supports the end-to-end simulation of the system considering factors such as battery life and realistic UAS kinematics. The implementation of the system leads to new findings needed to deploy UAS fleets in urban environments.
With the correct permits in place, further research can explore how different UAS network topologies behave in an urban environment when implemented with off the shelf UAS hardware. In addition to testing different network topologies, this thesis covers the implementation of building a secure, scalable system using modern cloud computation tools and services capable of supporting a variable number of UAS. The system also supports the end-to-end simulation of the system considering factors such as battery life and realistic UAS kinematics. The implementation of the system leads to new findings needed to deploy UAS fleets in urban environments.
ContributorsD'Souza, Daniel (Author) / Panchanathan, Sethuraman (Thesis advisor) / Berman, Spring (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2018
Description
Human-Robot collaboration can be a challenging exercise especially when both the human and the robot want to work simultaneously on a given task. It becomes difficult for the human to understand the intentions of the robot and vice-versa. To overcome this problem, a novel approach using the concept of Mixed-Reality has been proposed, which uses the surrounding space as the canvas to augment projected information on and around 3D objects. A vision based tracking algorithm precisely detects the pose and state of the 3D objects, and human-skeleton tracking is performed to create a system that is both human-aware as well as context-aware. Additionally, the system can warn humans about the intentions of the robot, thereby creating a safer environment to work in. An easy-to-use and universal visual language has been created which could form the basis for interaction in various human-robot collaborations in manufacturing industries.
An objective and subjective user study was conducted to test the hypothesis, that using this system to execute a human-robot collaborative task would result in higher performance as compared to using other traditional methods like printed instructions and through mobile devices. Multiple measuring tools were devised to analyze the data which finally led to the conclusion that the proposed mixed-reality projection system does improve the human-robot team's efficiency and effectiveness and hence, will be a better alternative in the future.
An objective and subjective user study was conducted to test the hypothesis, that using this system to execute a human-robot collaborative task would result in higher performance as compared to using other traditional methods like printed instructions and through mobile devices. Multiple measuring tools were devised to analyze the data which finally led to the conclusion that the proposed mixed-reality projection system does improve the human-robot team's efficiency and effectiveness and hence, will be a better alternative in the future.
ContributorsRathore, Yash K (Author) / Amor, Hani Ben (Thesis advisor) / Nelson, Brian (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
Several physical systems exist in the real world that involve continuous as well as discrete changes. These range from natural dynamic systems like the system of a bouncing ball to robotic dynamic systems such as planning the motion of a robot across obstacles. The key aspects of effectively describing such dynamic systems is to be able to plan and verify the evolution of the continuous components of the system while simultaneously maintaining critical constraints. Developing a framework that can effectively represent and find solutions to such physical systems prove to be highly advantageous. Both hybrid automata and action languages are formal models for describing the evolution of dynamic systems. The action language C+ is a rich and expressive language framework to formalize physical systems, but can be used only with physical systems in the discrete domain and is limited in its support of continuous domain components of such systems. Hybrid Automata is a well established formalism used to represent such complex physical systems at a theoretical level, however it is not expressive enough to capture the complex relations between the components of the system the way C+ does.
This thesis will focus on establishing a formal relationship between these two formalisms by showing how to succinctly represent Hybrid Automata in an action language which in turn is defined as a high-level notation for answer set programming modulo theories (ASPMT) --- an extension of answer set programs in the first-order level. Furthermore, this encoding framework is shown to be more effective and expressive than Hybrid Automata by highlighting its ability in allowing states of a hybrid transition system to be defined by complex relations among components that would otherwise be abstracted away in Hybrid Automata. The framework is further realized in the implementation of the system CPLUS2ASPMT, which takes advantage of state of the art ODE(Ordinary Differential Equations) based SMT solver dReal to provide support for ODE based evolution of continuous components of a dynamic system.
This thesis will focus on establishing a formal relationship between these two formalisms by showing how to succinctly represent Hybrid Automata in an action language which in turn is defined as a high-level notation for answer set programming modulo theories (ASPMT) --- an extension of answer set programs in the first-order level. Furthermore, this encoding framework is shown to be more effective and expressive than Hybrid Automata by highlighting its ability in allowing states of a hybrid transition system to be defined by complex relations among components that would otherwise be abstracted away in Hybrid Automata. The framework is further realized in the implementation of the system CPLUS2ASPMT, which takes advantage of state of the art ODE(Ordinary Differential Equations) based SMT solver dReal to provide support for ODE based evolution of continuous components of a dynamic system.
ContributorsLoney, Nikhil (Author) / Lee, Joohyung (Thesis advisor) / Fainekos, Georgios (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2017
Description
This work presents a communication paradigm, using a context-aware mixed reality approach, for instructing human workers when collaborating with robots. The main objective of this approach is to utilize the physical work environment as a canvas to communicate task-related instructions and robot intentions in the form of visual cues. A vision-based object tracking algorithm is used to precisely determine the pose and state of physical objects in and around the workspace. A projection mapping technique is used to overlay visual cues on tracked objects and the workspace. Simultaneous tracking and projection onto objects enables the system to provide just-in-time instructions for carrying out a procedural task. Additionally, the system can also inform and warn humans about the intentions of the robot and safety of the workspace. It was hypothesized that using this system for executing a human-robot collaborative task will improve the overall performance of the team and provide a positive experience to the human partner. To test this hypothesis, an experiment involving human subjects was conducted and the performance (both objective and subjective) of the presented system was compared with a conventional method based on printed instructions. It was found that projecting visual cues enabled human subjects to collaborate more effectively with the robot and resulted in higher efficiency in completing the task.
ContributorsKalpagam Ganesan, Ramsundar (Author) / Ben Amor, Hani (Thesis advisor) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2017
Description
There have been multiple attempts of coupling neural networks with external memory components for sequence learning problems. Such architectures have demonstrated success in algorithmic, sequence transduction, question-answering and reinforcement learning tasks. Most notable of these attempts is the Neural Turing Machine (NTM), which is an implementation of the Turing Machine with a neural network controller that interacts with a continuous memory. Although the architecture is Turing complete and hence, universally computational, it has seen limited success with complex real-world tasks.
In this thesis, I introduce an extension of the Neural Turing Machine, the Neural Harvard Machine, that implements a fully differentiable Harvard Machine framework with a feed-forward neural network controller. Unlike the NTM, it has two different memories - a read-only program memory and a read-write data memory. A sufficiently complex task is divided into smaller, simpler sub-tasks and the program memory stores parameters of pre-trained networks trained on these sub-tasks. The controller reads inputs from an input-tape, uses the data memory to store valuable signals and writes correct symbols to an output tape. The output symbols are a function of the outputs of each sub-network and the state of the data memory. Hence, the controller learns to load the weights of the appropriate program network to generate output symbols.
A wide range of experiments demonstrate that the Harvard Machine framework learns faster and performs better than the NTM and RNNs like LSTM, as the complexity of tasks increases.
In this thesis, I introduce an extension of the Neural Turing Machine, the Neural Harvard Machine, that implements a fully differentiable Harvard Machine framework with a feed-forward neural network controller. Unlike the NTM, it has two different memories - a read-only program memory and a read-write data memory. A sufficiently complex task is divided into smaller, simpler sub-tasks and the program memory stores parameters of pre-trained networks trained on these sub-tasks. The controller reads inputs from an input-tape, uses the data memory to store valuable signals and writes correct symbols to an output tape. The output symbols are a function of the outputs of each sub-network and the state of the data memory. Hence, the controller learns to load the weights of the appropriate program network to generate output symbols.
A wide range of experiments demonstrate that the Harvard Machine framework learns faster and performs better than the NTM and RNNs like LSTM, as the complexity of tasks increases.
ContributorsBhatt, Manthan Bharat (Author) / Ben Amor, Hani (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020