Mehdi Samadi

CoBots: Collaborative robots servicing multi-floor buildings

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

Learning Task Knowledge from Dialog and Web Access

We present KnoWDiaL, an approach for Learning and using task-relevant Knowledge from human-robot Dialog and access to the Web. KnoWDiaL assumes that there is an autonomous agent that performs tasks, as requested by humans through speech. The agent needs to “understand” the request, (i.e., to fully ground the task until it can proceed to plan for and execute it). KnoWDiaL contributes such understanding by using and updating a Knowledge Base, by dialoguing with the user, and by accessing the web. We believe that KnoWDiaL, as we present it, can be applied to general autonomous agents. However, we focus on our work with our autonomous collaborative robot, CoBot, which executes service tasks in a building, moving around and transporting objects between locations. Hence, the knowledge acquired and accessed consists of groundings of language to robot actions, and building locations, persons, and objects. KnoWDiaL handles the interpretation of voice commands, is robust regarding speech recognition errors, and is able to learn commands involving referring expressions in an open domain, (i.e., without requiring a lexicon). We present in detail the multiple components of KnoWDiaL, namely a frame-semantic parser, a probabilistic grounding model, a web-based predicate evaluator, a dialog manager, and the weighted predicate-based Knowledge Base. We illustrate the knowledge access and updates from the dialog and Web access, through detailed and complete examples. We further evaluate the correctness of the predicate instances learned into the Knowledge Base, and show the increase in dialog efficiency as a function of the number of interactions. We have extensively and successfully used KnoWDiaL in CoBot dialoguing and accessing the Web, and extract a few corresponding example sequences from captured videos.

Extending the Applicability of Pattern and Endgame Databases

For most high-performance two-player game programs, a significant amount of time is devoted to developing the evaluation function. An important issue in this regard is how to take advantage of a large memory. For some two-player games, endgame databases have been an effective way of reducing search effort and introducing accurate values into the search. For some one-player games (single-agent domains or puzzles), pattern databases have been effective at improving the quality of the heuristic values used in a search. This paper introduces new ways to extend the utility of pattern and endgame databases. Through the use of abstraction: (1) single-agent pattern databases can be applied to two- or more-player games; knowledge of the capabilities of one player (being oblivious to the opponent) can be an effective evaluation function for a class of game domains, and (2) endgame database positions can be viewed as an abstraction of more complicated positions; database lookups can be used as evaluation function features. These ideas are illustrated using the games of Chinese Checkers, Chess, and Thief and Police. For each domain, even small databases can be used to produce strong game play. This research has relevance to the recent interest in building general game-playing (GGP) programs. For two- or more-player applications where pattern and/or endgame databases can be built, abstraction can be used to automatically construct an evaluation function.