Nishant Shukla — accepted AI-conference papers

2017

Learning Human Utility from Video Demonstrations for Deductive Planning in Robotics

Nishant Shukla, Yunzhong He, Frank Chen, Song-Chun Zhu

CoRL 2017

We uncouple three components of autonomous behavior (utilitarian value, causal reasoning, and fine motion control) to design an interpretable model of tasks from video demonstrations. Utilitarian value is learned from aggregating human preferences to understand the implicit goal of a task, explainin

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2016

Robot learning with a spatial, temporal, and causal and-or graph

Caiming Xiong, Nishant Shukla, Wenlong Xiong, Song-Chun Zhu

ICRA 2016

We propose a stochastic graph-based framework for a robot to understand tasks from human demonstrations and perform them with feedback control. It unifies both knowledge representation and action planning in the same hierarchical data structure, allowing a robot to expand its spatial, temporal, and

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