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  • PhD Dissertation Defense - Heramb Nemlekar

    Thu, May 18, 2023 @ 11:30 AM - 01:30 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Dissertation Defense - Heramb Nemlekar

    Committee: Gaurav Sukhatme, Heather Culbertson, Jyotirmoy Deshmukh, Satyandra K. Gupta, Stefanos Nikolaidis (Chair)

    Title: Efficiently Learning Human Preferences for Proactive Robot Assistance in Assembly Tasks

    Robots that support humans in collaborative tasks need to adapt to the individual preferences of their human partners efficiently. While prior work has mainly focused on learning human preferences from demonstrations in the actual task, obtaining this data can be expensive in real world settings such as assembly and manufacturing. Thus, this dissertation proposes leveraging prior knowledge of (i) similarities in the preferences of different users in a given task and (ii) similarities in the preferences of a given user in different tasks for efficient robot adaptation. Firstly, to leverage similarities between users, we propose a two stage approach for clustering user demonstrations to identify the dominant models of user preferences in complex assembly tasks. This allows assistive robots to efficiently infer the preferences of new users by matching their actions to a dominant preference model. We evaluate our approach in an IKEA assembly study and show that it can improve the accuracy of predicting user actions by quickly inferring the user preference. Next, to leverage similarities between tasks, we propose learning user preferences as a function of task agnostic features (e.g., the mental and physical effort of user actions) from demonstrations in a short canonical task and transferring the preferences to the actual assembly. Obtaining demonstrations in a canonical task requires less time and human effort, allowing robots to learn user preferences efficiently. In a user study with a manually designed canonical task and an actual task of assembling a model airplane, we observe that our approach can predict user actions in the actual assembly based on the task agnostic preferences learned in the canonical task. We extend our approach to account for users that change their preferences when switching tasks, by updating the transferred user preferences during the actual task. In a human to robot assembly study, we demonstrate how an assistive robot can adapt to the changing preferences of users and proactively support them, thereby reducing their idle time and enhancing their collaborative experience. Lastly, we propose a method to automatically select a canonical task suitable for the transfer learning of human preferences based on the expressiveness of the task. Our experiments show that transferring user preferences from a short but expressive canonical task improves the accuracy of predicting user actions in longer actual tasks. Overall, this dissertation proposes and evaluates novel approaches for efficiently adapting to human preferences, which can enhance the productivity and satisfaction of human workers in real-world assemblies.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/91591350584?pwd=a2lRcE9peGFCeFBLa05sRW1vT25UUT09


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