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Creators/Authors contains: "Gaurav, Sanket"

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  1. ; (, International Conference on Autonomous Agents and Multiagent Systems)
    More accurately inferring human intentions/goals can help robots complete collaborative human-robot tasks more safely and efficiently. Bayesian reasoning has become a popular approach for predicting the intention or goal of a partial sequence of actions/controls using a trajectory likelihood model. However, the mismatch between the training objective for these models (maximizing trajectory likelihood) and the application objective (maximizing intention likelihood) can be detrimental. In this paper, we seek to improve the goal prediction of maximum entropy inverse reinforcement learning (MaxEnt IRL) models by training to maximize goal likelihood. We demonstrate the benefits of our method on pointing task goal prediction with multiple possible goals and predicting goal based activities in the Cornell Activity Dataset (CAD-120). 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids))
    By projecting into a 3-D workspace, robotic teleoperation using virtual reality allows for a more intuitive method of control for the operator than using a 2-D view from the robot's visual sensors. This paper investigates a setup that places the teleoperator in a virtual representation of the robot's environment and develops a deep learning based architecture modeling the correspondence between the operator's movements in the virtual space and joint angles for a humanoid robot using data collected from a series of demonstrations. We evaluate the correspondence model's performance in a pick-and - place teleoperation experiment. 
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    Full Text Available