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  • PhD Thesis Defense - Gautam Salhotra

    Tue, Dec 05, 2023 @ 03:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

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    PhD Thesis Defense - Gautam Salhotra
     
    Committee Members: Gaurav Sukhatme (chair), Somil Bansal, Daniel Seita
     
    Title: Accelerating Robot Manipulation with demonstrations
     
    Abstract: Robot manipulation of complex objects, such as cloth, is challenging due to difficulties in perceiving and exploring the environment. Pure reinforcement learning (RL) is difficult in this setting, as it requires extensive exploration of the state space, which can be inefficient and dangerous. Demonstrations from humans can alleviate the need for exploration, but collecting good demonstrations can be time-consuming and expensive. Therefore, a good balance between perception, exploration, and imitation is needed to solve manipulation of complex objects.This thesis focuses on dexterous manipulation of complex objects, such as cloth, using images and without assuming full state information during inference. It also aims to achieve efficient learning by reducing interactions with the environment during exploration and reducing the overhead of collecting demonstrations. To achieve these goals, we present i. a learning algorithm that uses a motion planner in the loop, to enable efficient long horizon exploration, ii. A framework for visual manipulation of complex deformable objects using demonstrations from a set of agents with different embodiments. iii. An LfD algorithm for dexterous tasks with rigid objects, such as peg insertion with high precision, using images and a multi-task attention-based architecture.These contributions enable robots to manipulate complex objects efficiently and with high precision, using images alone. This opens up new possibilities for robots to be used in a wider range of applications, such as manufacturing, logistics, and healthcare

    Location: Ronald Tutor Hall of Engineering (RTH) - 406

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

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