Craig Lee

Pheromone Robotics

We describe techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. We exploit the biologically inspired notion of a “virtual pheromone,” implemented using simple transceivers mounted atop each robot. Unlike the chemical markers used by insect colonies for communication and coordination, our virtual pheromones are symbolic messages tied to the robots themselves rather than to fixed locations in the environment. This enables our robot collective to become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. This leads to notions of world-embedded computation and world-embedded displays that provide different ways to think about robot colonies and the types of distributed computations that such colonies might perform.

Distributed design review in virtual environments

In large distributed corporations, distributed design review offers the potential for cost savings, reduced time to market, and improved efficiency. It also has the potential to improve the design process by enabling wider expertise to be incorporated in design reviews. This paper describes the integration of several components to enable distributed virtual design review in mixed multi-party, heterogeneous multi-site 2D and immersive 3D environments. The system provides higher layers of support for collaboration including avatars, high fidelity audio, and shared artifact manipulation. The system functions across several interface environments ranging from CAVEs to Walls to desktop workstations. At the center of the software architecture is the Human Integrating Virtual Environment (HIVE) [6], a collaboration infrastructure and toolset to support research and development of multi-user, geographically distributed, 2D and 3D shared applications. The HIVE functions with VisualEyes software for visualizing 3D data in virtual environments. We also describe in detail the configuration and lessons learned in a two site, heterogeneous multi-user demonstration of the system between HRL Laboratories in Malibu, California and GM R&D in Warren, Michigan.

Detecting future social unrest in unprocessed Twitter data: “Emerging phenomena and big data”

We have implemented a social media data mining system capable of forecasting events related to Latin American social unrest. Our method directly extracts a small number of tweets from publicly-available data on twitter.com, condenses similar tweets into coherent forecasts, and assembles a detailed and easily-interpretable audit trail which allows end users to quickly collect information about an upcoming event. Our system functions by continually applying multiple textual and geographic filters to a large volume of data streaming from twitter.com via the public API as well as a commercial data feed. To be specific, we search the entirety of twitter.com for a few carefully chosen keywords, search within those tweets for mentions of future dates, filter again using various logistic regression classifiers, and finally assign a location to an event by geocoding retweeters. Geocoding is done using our previously-developed in-house geocoding service which, at the time of this writing, can infer the home location for over 62M twitter.com users [1]. Additionally, we identify demographics likely interested in an upcoming event by searching retweeters recent posts for demographic-specific keywords.

Using publicly visible social media to build detailed forecasts of civil unrest

We demonstrate how one can generate predictions for several thousand incidents of Latin American civil unrest, often many days in advance, by surfacing informative public posts available on Twitter and Tumblr.The data mining system presented here runs daily and requires no manual intervention. Identification of informative posts is accomplished by applying multiple textual and geographic filters to a high-volume data feed consisting of tens of millions of posts per day which have been flagged as public by their authors. Predictions are built by annotating the filtered posts, typically a few dozen per day, with demographic, spatial, and temporal information.Key to our textual filters is the fact that social media posts are necessarily short, making it possible to easily infer topic by simply searching for comentions of typically unrelated terms within the same post (e.g. a future date comentioned with an unrest keyword). Additional textual filters then proceed by applying a logistic regression classifier trained to recognize accounts belonging to organizations who are likely to announce civil unrest.Geographic filtering is accomplished despite sparsely available GPS information and without relying on sophisticated natural language processing. A geocoding technique which infers non-GPS-known user locations via the locations of their GPS-known friends provides us with location estimates for 91,984,163 Twitter users at a median error of 6.65km. We show that announcements of upcoming events tend to localize within a small geographic region, allowing us to forecast event locations which are not explicitly mentioned in text.We annotate our forecasts with demographic information by searching the collected posts for demographic specific keywords generated by hand as well as with the aid of DBpedia.Our system has been in production since December 2012 and, at the time of this writing, has produced 4,771 distinct forecasts for events across ten Latin American nations. Manual examination of 2,859 posts surfaced by our method revealed that only 108 were discussing topics unrelated to civil unrest. Examination of 2,596 forecasts generated between 2013-07-01 and 2013-11-30 found 1,192 (45.9%) matched exactly the date and within a 100 km radius of a civil unrest event reported in traditional news media.

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with real robots

This paper describes a redundant robot arm that is capable of learning to reach for targets in space while avoiding obstacles in a self-organized fashion. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle free space using the direction-to-rotation transform (DIRECT). The DIRECT based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not experiencing them during learning. We have developed a DIRECT-based reactive obstacle avoidance controller (DIRECT-ROAC) that enables the redundant robot arm to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevent the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, we model a self-organized process of mental rehearsals of movements inspired by human and animal experiments on reaching to generate plans for movement execution using DIRECT-ROAC in complex environments. These mental rehearsals or plans are self generated by utilizing perceptual information in the form of via-points extracted from attentional shrouds around obstacles in its environment. Computer simulations show that the proposed novel controller is successful in avoiding obstacles in environments with complex obstacle configurations.