Kartik Talamadupula

Planning for human-robot teaming in open worlds

As the number of applications for human-robot teaming continue to rise, there is an increasing need for planning technologies that can guide robots in such teaming scenarios. In this article, we focus on adapting planning technology to Urban Search And Rescue (USAR) with a human-robot team. We start by showing that several aspects of state-of-the-art planning technology, including temporal planning, partial satisfaction planning, and replanning, can be gainfully adapted to this scenario. We then note that human-robot teaming also throws up an additional critical challenge, namely, enabling existing planners, which work under closed-world assumptions, to cope with the open worlds that are characteristic of teaming problems such as USAR. In response, we discuss the notion of conditional goals, and describe how we represent and handle a specific class of them called open world quantified goals. Finally, we describe how the planner, and its open world extensions, are integrated into a robot control architecture, and provide an empirical evaluation over USAR experimental runs to establish the effectiveness of the planning components.

Planning for serendipity

Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity - i.e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a real-life implementation of the ideas on the Nao Robot interacting with a human colleague.

Finding and exploiting goal opportunities in real-time during plan execution

Autonomous robots that operate in real-world domains face multiple challenges that make planning and goal selection difficult. Not only must planning and execution occur in real time, newly acquired knowledge can invalidate previous plans, and goals and their utilities can change during plan execution. However, these events can also provide opportunities, if the architecture is designed to react appropriately. We present here an architecture that integrates the SapaReplan planner with the DIARC robot architecture, allowing the architecture to react dynamically to changes in the robots goal structures.

RAProp: ranking tweets by exploiting the tweet/user/web ecosystem and inter-tweet agreement

The increasing popularity of Twitter renders improved trust- worthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweets? content alone. We present a novel ranking method called RAProp, which combines two orthogonal measures of relevance and trustworthiness of a tweet. The first, called Feature Score, measures the trustworthiness of the source of the tweet by extracting features from a 3-layer Twitter ecosystem consisting of users, tweets and webpages. The second measure, called agreement analysis, estimates the trustworthiness of the content of a tweet by analyzing whether the content is independently corroborated by other tweets. We view the candidate result set of tweets as the vertices of a graph, with the edges measuring the estimated agreement between each pair of tweets. The feature score is propagated over this agreement graph to compute the top-k tweets that have both trustworthy sources and independent corroboration. The evaluation of our method on 16 million tweets from the TREC 2011 Microblog Dataset shows that for top-30 precision, we achieve 53% better precision than the current best performing method on the data set, and an improvement of 300% over current Twitter Search.

A knowledge driven policy framework for internet of things

With the proliferation of technology, connected and interconnected devices (henceforth referred to as IoT) are fast becoming a viable option to automate the day-to-day interactions of users with their environment—be it manufacturing or home-care automation. However, with the explosion of IoT deployments we have observed in recent years, manually governing the interactions between humans-to-devices—and especially devices-to- devices—is an impractical task, if not an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equip to maintain a bird’s-eye-view of the interaction space. Motivated by this observation, in this paper, we propose an end-to-end framework that (a) automatically dis- covers devices, and their associated services and capabilities w.r.t. an ontology; (b) supports representation of high-level—and expressive—user policies to govern the devices and services in the environment; (c) pro- vides efficient procedures to refine and reason about policies to automate the management of interactions; and (d) delegates similar capable devices to fulfill the interactions, when conflicts occur. We then present our initial work in instrumenting the framework and discuss its details.

Policy conflict resolution in IoT via planning

With the explosion of connected devices to automate tasks, manually governing interactions among such devices—and associated services—has become an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equipped to maintain a bird’s-eye-view of the environment. Motivated by this observation, in this paper, we present an ontology-based policy framework which can efficiently detect policy conflicts and automatically resolve such using an AI planner.