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- All Subjects: Human-Robot Interaction
- All Subjects: Planning--Automation.
- Creators: Kambhampati, Subbarao
Description
In this dissertation I develop a deep theory of temporal planning well-suited to analyzing, understanding, and improving the state of the art implementations (as of 2012). At face-value the work is strictly theoretical; nonetheless its impact is entirely real and practical. The easiest portion of that impact to highlight concerns the notable improvements to the format of the temporal fragment of the International Planning Competitions (IPCs). Particularly: the theory I expound upon here is the primary cause of--and justification for--the altered (i) selection of benchmark problems, and (ii) notion of "winning temporal planner". For higher level motivation: robotics, web service composition, industrial manufacturing, business process management, cybersecurity, space exploration, deep ocean exploration, and logistics all benefit from applying domain-independent automated planning technique. Naturally, actually carrying out such case studies has much to offer. For example, we may extract the lesson that reasoning carefully about deadlines is rather crucial to planning in practice. More generally, effectively automating specifically temporal planning is well-motivated from applications. Entirely abstractly, the aim is to improve the theory of automated temporal planning by distilling from its practice. My thesis is that the key feature of computational interest is concurrency. To support, I demonstrate by way of compilation methods, worst-case counting arguments, and analysis of algorithmic properties such as completeness that the more immediately pressing computational obstacles (facing would-be temporal generalizations of classical planning systems) can be dealt with in theoretically efficient manner. So more accurately the technical contribution here is to demonstrate: The computationally significant obstacle to automated temporal planning that remains is just concurrency.
ContributorsCushing, William Albemarle (Author) / Kambhampati, Subbarao (Thesis advisor) / Weld, Daniel S. (Committee member) / Smith, David E. (Committee member) / Baral, Chitta (Committee member) / Davalcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Current work in planning assumes that user preferences and/or domain dynamics are completely specified in advance, and aims to search for a single solution plan to satisfy these. In many real world scenarios, however, providing a complete specification of user preferences and domain dynamics becomes a time-consuming and error-prone task. More often than not, a user may provide no knowledge or at best partial knowledge of her preferences with respect to a desired plan. Similarly, a domain writer may only be able to determine certain parts, not all, of the model of some actions in a domain. Such modeling issues requires new concepts on what a solution should be, and novel techniques in solving the problem. When user preferences are incomplete, rather than presenting a single plan, the planner must instead provide a set of plans containing one or more plans that are similar to the one that the user prefers. This research first proposes the usage of different measures to capture the quality of such plan sets. These are domain-independent distance measures based on plan elements if no knowledge of the user preferences is given, or the Integrated Preference Function measure in case incomplete knowledge of such preferences is provided. It then investigates various heuristic approaches to generate plan sets in accordance with these measures, and presents empirical results demonstrating the promise of the methods. The second part of this research addresses planning problems with incomplete domain models, specifically those annotated with possible preconditions and effects of actions. It formalizes the notion of plan robustness capturing the probability of success for plans during execution. A method of assessing plan robustness based on the weighted model counting approach is proposed. Two approaches for synthesizing robust plans are introduced. The first one compiles the robust plan synthesis problems to the conformant probabilistic planning problems. The second approximates the robustness measure with lower and upper bounds, incorporating them into a stochastic local search for estimating distance heuristic to a goal state. The resulting planner outperforms a state-of-the-art planner that can handle incomplete domain models in both plan quality and planning time.
ContributorsNguyễn, Tuấn Anh (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Do, Minh (Committee member) / Lee, Joohyung (Committee member) / Smith, David E. (Committee member) / Arizona State University (Publisher)
Created2014
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 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.
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
Description
Automated planning problems classically involve finding a sequence of actions that transform an initial state to some state satisfying a conjunctive set of goals with no temporal constraints. But in many real-world problems, the best plan may involve satisfying only a subset of goals or missing defined goal deadlines. For example, this may be required when goals are logically conflicting, or when there are time or cost constraints such that achieving all goals on time may be too expensive. In this case, goals and deadlines must be declared as soft. I call these partial satisfaction planning (PSP) problems. In this work, I focus on particular types of PSP problems, where goals are given a quantitative value based on whether (or when) they are achieved. The objective is to find a plan with the best quality. A first challenge is in finding adequate goal representations that capture common types of goal achievement rewards and costs. One popular representation is to give a single reward on each goal of a planning problem. I further expand on this approach by allowing users to directly introduce utility dependencies, providing for changes of goal achievement reward directly based on the goals a plan achieves. After, I introduce time-dependent goal costs, where a plan incurs penalty if it will achieve a goal past a specified deadline. To solve PSP problems with goal utility dependencies, I look at using state-of-the-art methodologies currently employed for classical planning problems involving heuristic search. In doing so, one faces the challenge of simultaneously determining the best set of goals and plan to achieve them. This is complicated by utility dependencies defined by a user and cost dependencies within the plan. To address this, I introduce a set of heuristics based on combinations using relaxed plans and integer programming formulations. Further, I explore an approach to improve search through learning techniques by using automatically generated state features to find new states from which to search. Finally, the investigation into handling time-dependent goal costs leads us to an improved search technique derived from observations based on solving discretized approximations of cost functions.
ContributorsBenton, J (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Do, Minh B. (Committee member) / Smith, David E. (Committee member) / Langley, Pat (Committee member) / Arizona State University (Publisher)
Created2012
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
This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This architecture was designed modularly, allowing the use of different planners and robots. The system automatically replans when robots connect or disconnect. The system was demonstrated on two real robots, a Fetch and a PeopleBot, by conducting a surveillance task on the fifth floor of the Computer Science building at Arizona State University. The next part of the system includes extensions for teaming with humans. An Android application was created to serve as the interface between the system and human teammates. This application provides a way for the system to communicate with humans in the loop. In addition, it sends location information of the human teammates to the system so that goal recognition can be performed. This goal recognition allows the generation of human-aware plans. This capability was demonstrated in a mock search and rescue scenario using the Fetch to locate a missing teammate.
ContributorsSaba, Gabriel Christer (Author) / Kambhampati, Subbarao (Thesis director) / Doupé, Adam (Committee member) / Chakraborti, Tathagata (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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 this work, I propose to bridge the gap between human users and adaptive control of robotic systems. The goal is to enable robots to consider user feedback and adjust their behaviors. A critical challenge with designing such systems is that users are often non-experts, with limited knowledge about the robot's hardware and dynamics. In the domain of human-robot interaction, there exist different modalities of conveying information regarding the desired behavior of the robot, most commonly used are demonstrations, and preferences. While it is challenging for non-experts to provide demonstrations of robot behavior, works that consider preferences expressed as trajectory rankings lead to users providing noisy and possibly conflicting information, leading to slow adaptation or system failures. The end user can be expected to be familiar with the dynamics and how they relate to their desired objectives through repeated interactions with the system. However, due to inadequate knowledge about the system dynamics, it is expected that the user would find it challenging to provide feedback on all dimension's of the system's behavior at all times. Thus, the key innovation of this work is to enable users to provide partial instead of completely specified preferences as with traditional methods that learn from user preferences. In particular, I consider partial preferences in the form of preferences over plant dynamic parameters, for which I propose Adaptive User Control (AUC) of robotic systems. I leverage the correlations between the observed and hidden parameter preferences to deal with incompleteness. I use a sparse Gaussian Process Latent Variable Model formulation to learn hidden variables that represent the relationships between the observed and hidden preferences over the system parameters. This model is trained using Stochastic Variational Inference with a distributed loss formulation. I evaluate AUC in a custom drone-swarm environment and several domains from DeepMind control suite. I compare AUC with the state-of-the-art preference-based reinforcement learning methods that are utilized with user preferences. Results show that AUC outperforms the baselines substantially in terms of sample and feedback complexity.
ContributorsBiswas, Upasana (Author) / Zhang, Yu (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Berman, Spring (Committee member) / Liu, Lantao (Committee member) / Arizona State University (Publisher)
Created2023
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