Task planning, execution, and prediction-based coordination with the human wearer

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 87-89).Full automation of repetitive and/or specialized tasks has become a preferred means to meet the needs of manufacturing industries. However, some tasks cannot be fully automated due to their complexity or the nature of the work environment. In such cases, semi-automation through human-robot collaboration is a strong alternative that still maintains a high level of efficiency in task execution. This thesis focused on the control and coordination issues of the Supernumerary Robotic Limbs (SRL); a pair of wearable robotic limbs that are a potential solution to these issues. The first purpose of this study was to adequately model the collaborative aspect of a task that is conventionally performed by two coworkers. This was achieved through the Coloured Petri Nets (CPN) tool, which was able to model the collaboration between two coworkers by using the SRL and its operator instead. The second purpose of this work was to evaluate how to implement a sensor suit to establish reliable communication between the SRL and its operator. Using data-driven methods for detection, we were able to monitor the operator's current state. By combining this data with the CPN task model we were able to relay the operator's intentions to the SRL. This enabled the SRL to follow the CPN process model in a timely and coordinated manner together with its operator. The third and final section of this thesis focused on considering the interchangeability of roles between the SRL and its operator. We used a datadriven approach to model a task where the SRL and its operator had to perform a simultaneous dynamic task. This was performed by using teach by demonstration techniques on process data from two workers. A control algorithm was then extracted from the actions of the supporting worker. Both the process model and the sensor suit, together with the detection algorithms, were implemented and validated using the first prototype of the SRL. Results show that the SRL was successful in autonomously coordinating with its operator and completing an intercostal assembly task.by Baldin Adolfo Llorens - Bonilla.S.M

Fluency and embodiment for robots acting with humans

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 225-234).This thesis is concerned with the notion of fluency in human-robot interaction (HRI), exploring cognitive mechanisms for robotic agents that would enable them to overcome the stop-and-go rigidity present in much of HRI to date. We define fluency as the ethereal yet manifest quality existent when two agents perform together at high level of coordination and adaptation, in particular when they are well-accustomed to the task and to each other. Based on mounting psychological and neurological evidence, we argue that one of the keys to this goal is the adaptation of an embodied approach to robot cognition. We show how central ideas from this psychological school are applicable to robot cognition and present a cognitive architecture making use of perceptual symbols, simulation, and perception-action networks. In addition, we demonstrate that anticipation of perceptual input, and in particular of the actions of others, are an important ingredient of fluent joint action. To that end, we show results from an experiment studying the effects of anticipatory action on fluency and teamwork, and use these results to suggest benchmark metrics for fluency. We also show the relationship between anticipatory action and a simulator approach to perception, through a comparative human subject study of an implemented cognitive architecture on the robot AUR, a robotic desk lamp, designed for this thesis. A result of this work is modeling the effect of practice on human-robot joint action, arguing that mechanisms that govern the passage of cognitive capabilities from a deliberate yet slower system to a faster, sub-intentional, and more rigid one, are crucial to fluent joint action in well-rehearsed ensembles. Theatrical acting theory serves as an inspiration for this work, as we argue that lessons from acting method can be applied to human-robot interaction.by Guy Hoffman.Ph.D

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Unsupervised learning and recognition of physical activity plans

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 125-129).This thesis desires to enable a new kind of interaction between humans and computational agents, such as robots or computers, by allowing the agent to anticipate and adapt to human intent. In the future, more robots may be deployed in situations that require collaboration with humans, such as scientific exploration, search and rescue, hospital assistance, and even domestic care. These situations require robots to work together with humans, as part of a team, rather than as a stand-alone tool. The intent recognition capability is necessary for computational agents to play a more collaborative role in human-robot interactions, moving beyond the standard master-slave relationship of humans and computers today. We provide an innovative capability for recognizing human intent, through statistical plan learning and online recognition. We approach the plan learning problem by employing unsupervised learning to automatically determine the activities in a plan based on training data. The plan activities are described by a mixture of multivariate probability densities. The number of distributions in the mixture used to describe the data is assumed to be given. The training data trajectories are fed again through the activities' density distributions to determine each possible sequence of activities that make up a plan. These activity sequences are then summarized with temporal information in a temporal plan network, which consists of a network of all possible plans. Our approach to plan recognition begins with formulating the temporal plan network as a hidden Markov model. Next, we determine the most likely path using the Viterbi algorithm. Finally, we refer back to the temporal plan network to obtain predicted future activities. Our research presents several innovations:(cont.) First, we introduce a modified representation of temporal plan networks that incorporates probabilistic information into the state space and temporal representations. Second, we learn plans from actual data, such that the notion of an activity is not arbitrarily or manually defined, but is determined by the characteristics of the data. Third, we develop a recognition algorithm that can perform recognition continuously by making probabilistic updates. Finally, our recognizer not only identifies previously executed activities, but also pre-dicts future activities based on the plan network. We demonstrate the capabilities of our algorithms on motion capture data. Our results show that the plan learning algorithm is able to generate reasonable temporal plan networks, depending on the dimensions of the training data and the recognition resolution used. The plan recognition algorithm is also successful in recognizing the correct activity sequences in the temporal plan network corresponding to the observed test data.by Shuonan Dong.S.M

Reusing a robot's behavioral mechanisms to model and manipulate human mental states

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 125-129).In a task domain characterized by physical actions and where information has value, competing teams gain advantage by spying on and deceiving an opposing team while cooperating teammates can help the team by secretly communicating new information. For a robot to thrive in this environment it must be able to perform actions in a manner to deceive opposing agents as well as to be able to secretly communicate with friendly agents. It must further be able to extract information from observing the actions of other agents. The goal of this research is to expand on current human robot interaction by creating a robot that can operate in the above scenario. To enable these behaviors, an architecture is created which provides the robot with mechanisms to work with hidden human mental states. The robot attempts to infer these hidden states from observable factors and use them to better understand and predict behavior. It also takes steps to alter them in order to change the future behavior of the other agent. It utilizes the knowledge that the human is performing analogous inferences about the robot's own internal states to predict the effect of its actions on the human's knowledge and perceptions of the robot. The research focuses on the implicit communication that is made possible by two embodied agents interacting in a shared space through nonverbal interaction. While the processes used by a robot differ significantly from the cognitive mechanisms employed by humans, each face the similar challenge of completing the loop from sensing to acting. This architecture employs a self-as-simulator strategy, reusing the robot's behavioral mechanisms to model aspects of the human's mental states. This reuse allows the robot to model human actions and the mental states behind them using the grammar of its own representations and actions.by Jesse Vail Gray.Ph.D

Discriminative, generative, and imitative learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 201-212).I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the Expectation-Maximization (EM) algorithm for latent discriminative learning (or latent MED).(cont.) While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior.by Tony Jebara.Ph.D

Supernumerary robotic limbs : biomechanical analysis and human-robot coordination Training

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 83-86).As the workforce within manufacturing grows older, especially within aircraft manufacturing, the need for new technologies to assist workers arises. If a technology could offer improvements to an aircraft manufacturing laborer's efficiency, as well as reduce the load on his body, it could potentially see vast use. This thesis discusses a potential solution to these issues - the Supernumerary Robotic Limbs (SRL). These limbs could potentially increase the workspace of the human operator to him more efficient, as well as reduce the load on the human while he performs staining tasks. It accomplishes this by providing the worker with extra arms in the form of a wearable backpack. This thesis first evaluates how the torques imposed on a human are affected when he uses an SRL-like device to help bear a static load. It is shown that the human work load necessary to bear such a load is reduced substantially. The second focus of this thesis is the skill acquisition. A data-driven approach is taken to learn trajectories and a leader-follower coordination relationship. This is done by generating teaching data representing trajectories and coordination information with two humans, then transferring the pertinent information to a robot that assumes the role of the follower. This coordination is validated in a simple one-dimension example, and is implemented on a robot that coordinates with a human leader during a control-box wiring task.by Clark Davenport.S.M

Resource allocation and optimal release time in software systems

Software quality is directly correlated with the number of defects in software systems. As the complexity of software increases, manual inspection of software becomes prohibitively expensive. Thus, defect prediction is of paramount importance to project managers in allocating the limited resources effectively as well as providing many advantages such as the accurate estimation of project costs and schedules. This thesis addresses the issues of statistical fault prediction modeling, software resource allocation, and optimal software release and maintenance policy. A software defect prediction model using operating characteristic curves is presented. The main idea behind this predictor is to use geometric insight in helping construct an efficient prediction method to reliably predict the cumulative number of defects during the software development process. Motivated by the widely used concept of queue models in communication systems and information processing systems, a resource allocation model which answers managerial questions related to project status and scheduling is then introduced. Using the proposed allocation model, managers will be more certain about making resource allocation decisions as well as measuring the system reliability and the quality of service provided to customers in terms of the expected response time. Finally, a novel stochastic model is proposed to describe the cost behavior of the operation, and estimate the optimal time by minimizing a cost function via artificial neural networks. Further, a detailed analysis of software release time and maintenance decision is also presented. The performance of the proposed approaches is validated on real data from actual SAP projects, and the experimental results demonstrate a compelling motivation for improved software qualit

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests