This paper explains an episodic-memory-based approach for computing anticipatory robot behavior in a partially observable environment. Inspired by biological findings on the mammalian hippocampus, here, episodic memories retain a sequence of experienced observation, behavior, and reward. Incorporating multiple machine learning methods, this approach attempts to help reducing the computational burden of a partially observable Markov decision process (POMDP) problem. In particular, proposed computational reduction techniques include: (1) abstraction of the state space via temporal difference learning; (2) abstraction of the action space by utilizing motor schemata; (3) narrowing down the state space in terms of goals through instance-based learning; (4) elimination of the value-iteration by assuming a unidirectional-linear-chaining formation of the state space; (5) reduction of the state-estimate computation by exploiting the property of the Poisson distribution; and (6) trimming the history length by imposing a cap on the number of episodes that are computed. Claims (5) and (6) were empirically verified, and it was confirmed that the state estimation can be in fact computed in an 0(n) time (where n is the number of the states), more efficient than a conventional Kalman-filter based approach of 0(n2).

As the capabilities, range of missions, and the size of robot teams increase, the ability for a human operator to account for all the factors in these complex scenarios can become exceedingly difficult. Our previous research has studied the use of case-based reasoning (CBR) tools to assist a user in the generation of multi-robot missions. These tools, however, typically assume that the robots available for the mission are of the same type (i.e., homogeneous). We loosen this assumption through the integration of contract-net protocol (CNP) based task allocation coupled with a CBR-based mission specification wizard. Two alternative designs are explored for combining case-based mission specification and CNP-based team allocation as well as the tradeoffs that result from the selection of one of these approaches over the other.

This paper describes a usability study designed to assess ease of use, user satisfaction, and performance of a mobile robot mission specification system. The software under consideration, MissionLab, allows users to specify a robot mission as well as compile it, execute it, and control the robot in real-time. In this work, a new automated mission repair mechanism that aids users in correcting faulty missions was added to the system. This mechanism was compared to an older version in order to better inform the development process, and set a direction for future improvements in usability.

MissionLab is a mission specification system that implements a hybrid deliberative and reactive control architecture for autonomous mobile robots. The user creates and executes the robot mission plans through its graphical user interface. As robot deployments become more common in highly stressful situations, such as in dealing with explosives or biohazards, the usability of their mission specification system becomes critical. To address this need, a mission-planning "wizard" has been recently integrated into MissionLab. By retrieving and adapting past successful mission plans stored in its database, this new feature is designed to simplify the user's planning process. The latest formal usability experiments, reported in this paper, testing for usability improvements in terms of speed of the mission planning process, accuracy of the produced mission plans, and ease of use is conducted. This paper introduces the mission-planning wizard, describes the usability experiments (including design), and discusses the results in detail.

This paper presents a method for a mobile robot to construct and localize relative to a "cognitive map", where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed.

This paper re-introduces and evaluates the schematic sowbug proposed by Tolman (1939). The schematic sowbug is based on Tolman's purposive behaviorism, and it is believed to be the first prototype in history that actually implemented a behavior-based architecture suitable for robotics. The schematic sowbug navigates the environment based on two types of vectors, orientation and progression, that are computed from the values of sensors perceiving stimuli. Our experiments on both simulation and real robot proved the legitimacy of Tolman's assumptions, and the potential of applying the schematic sowbug model and principles within modern robotics is recognized.