Zhiqiang Sui

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This paper presents an effort to enable robots to utilize open-source knowledge resources autonomously for human-robot interaction. The main challenges include how to extract knowledge in semi-structured and unstructured natural languages, how to make use of multiple types of knowledge in decision making, and how to identify the knowledge that is missing. A(More)
Users may ask a service robot to accomplish various tasks so that the designer of the robot cannot program each of the tasks beforehand. As more and more open-source knowledge resources become available, it is worthwhile trying to make use of open-source knowledge resources for service robots. The challenge lies in the autonomous identification, acquisition(More)
Manipulation tasks involving sequential pick-and-place actions in human environments remains an open problem for robotics. Central to this problem is the inability for robots to perceive in cluttered environments, where objects are physically touching, stacked, or occluded from the view. Such physical interactions currently prevent robots from(More)
As more and more open knowledge sources become available, it is interesting to explore opportunities of enhancing autonomous agents' capacities by utilizing the knowledge in these sources, instead of hand-coding knowledge for agents. A major challenge towards this goal lies in the translation of the open knowledge organized in multiple modes, unstructured(More)
This paper proposes a model of metareasoning for Human-Robot Interaction (HRI). Robots' basic abilities for HRI—planning, learning and dialogue—are characterized as three loops in the model, with each spanning ground, object and meta-level. The model provides a con-ceptualization of HRI and a framework for incremental development of large HRI systems such(More)
— Scene-level Programming by Demonstration (PbD) is faced with an important challenge-perceptual uncertainty. Addressing this problem, we present a scene-level PbD paradigm that programs robots to perform goal-directed manipulation in unstructured environments with grounded perception. Scene estimation is enabled by our discriminatively-informed generative(More)
— In order to perform autonomous sequential manipulation tasks, perception in cluttered scenes remains a critical challenge for robots. In this paper, we propose a probabilistic approach for robust sequential scene estimation and manipulation Sequential Scene Understanding and Manipulation (SUM). SUM considers uncertainty due to discriminative object(More)
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