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Shiqi Zhang
Researcher at Binghamton University
Publications - 226
Citations - 1387
Shiqi Zhang is an academic researcher from Binghamton University. The author has contributed to research in topics: Computer science & Large Hadron Collider. The author has an hindex of 15, co-authored 73 publications receiving 809 citations. Previous affiliations of Shiqi Zhang include Harbin Institute of Technology & Cleveland State University.
Papers
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Proceedings Article
Learning to interpret natural language commands through human-robot dialog
TL;DR: This work introduces a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from human-robot conversations by inducing training data from user paraphrases.
Journal ArticleDOI
BWIBots: A platform for bridging the gap between AI and human–robot interaction research:
Piyush Khandelwal,Shiqi Zhang,Shiqi Zhang,Jivko Sinapov,Matteo Leonetti,Matteo Leonetti,Jesse Thomason,Fangkai Yang,Ilaria Gori,Maxwell Svetlik,Priyanka Khante,Vladimir Lifschitz,Jake K. Aggarwal,Raymond J. Mooney,Peter Stone +14 more
TL;DR: A novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots designed as a part of the Building-Wide Intelligence project at the University of Texas at Austin is introduced.
Journal ArticleDOI
Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds
TL;DR: This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step toward addressing the challenges of deployment of robots in practical domains.
Proceedings Article
CORPP: commonsense reasoning and probabilistic planning, as applied to dialog with a mobile robot
Shiqi Zhang,Peter Stone +1 more
TL;DR: The CORPP algorithm is introduced which combines P-log, a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty and observes significant improvements in both efficiency and accuracy.
Journal ArticleDOI
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
TL;DR: In this article, the authors describe an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based descriptions of uncertainty and domain knowledge.