Learning Stable Task Sequences from Demonstration with Linear Parameter Varying Systems and Hidden Markov Models

The problem of acquiring multiple tasks from demonstration is typi- cally divided in two sequential processes: (1) the segmentation or identification of different subgoals/subtasks and (2) a separate learning process that parameterizes a control policy for each subtask. As a result, segmentation criteria typically neglect the characteristics of control policies and rely instead on simplified models. This paper aims for a single model capable of learning sequences of complex time-independent control policies that provide robust and stable behavior. To this end, we first present a novel and efficient approach to learn goal-oriented time independent motion models by estimating both attractor and dynamic behavior from data guaranteeing stability using linear parameter varying (LPV) systems. This method enables learning complex task sequences with hidden Markov models (HMMs), where each state/subtask is given by a stable LPV system and where transitions are most likely around the corresponding attractor. We study the dynamics of the HMM-LPV model and propose a motion generation method that guarantees the stability of task sequences. We validate our approach in two sets of demonstrated human motion

Analytic Performance Models for Parallel Discrete Event Battlefield Simulation with Conservative Synchronization

This study investigated the development and use of analytic models for performance analysis of parallel discrete event battlefield simulation using conservative synchronization. A simulation architecture with layered application, simulation, and host machine services provided the model development basis. Simulation entities were modeled with set-theoretic definitions. Deterministic performance models using these definitions were developed for event prediction, scheduling, and execution in sequential battlefield simulation. The sequential model was expanded to include relative bounds for overhead factors introduced when the simulation is spatially decomposed for a parallel distributed memory machine. Comparison of sequential and parallel models instantiated for a simulation with uniform workload showed a potential for unbounded processor blocking. A synchronization algorithm modification to limit per-iteration blocking is shown theoretically to decrease finishing time. Modification results were demonstrated on a hypercube architecture. Demonstration showed that a sequential simulation requiring 60 seconds to run was limited to a best time of 30 seconds on four processors without algorithm modification. The time was improved to 17 seconds using the modification. A number of basic timing measurements also showed that event list operations on a sequential structure take significantly longer than interactive event prediction algorithms using simulation entities maintained in similar structures

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Interprocess communication in highly distributed systems

Issued as Final technical report, Project no. G-36-632Final technical report has title: Interprocess communication in highly distributed system

Belief-Space Planning for Resourceful Manipulation and Mobility

Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use of series elastic actuators (SEAs) to improve manipulation capabilities and support safer operation around humans. The resourcefulness of these robots is complemented with a belief-space planning framework that enables task-driven action selection in the context of the partially observable environment. The framework uses a compact but expressive state representation based on object models. We extend an existing affordance-based object model, called an aspect transition graph (ATG), with geometric information. This enables object-centric modeling of features and actions, making the model much more expressive without increasing the complexity. A novel task representation enables the belief-space planner to perform general object-centric tasks ranging from recognition to manipulation of objects. The approach supports the efficient handling of multi-object scenes. The combination of the physical platform and the planning framework are evaluated in two novel, challenging, partially observable planning domains. The ARcube domain provides a large population of objects that are highly ambiguous. Objects can only be differentiated using multi-modal sensor information and manual interactions. In the dexterous mobility domain, a robot can employ multiple mobility modes to complete navigation tasks under a variety of possible environment constraints. The performance of the proposed approach is evaluated using experiments in simulation and on a real robot