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Browsing by Author "Mehta, Shaunak A."

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    Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation
    Mehta, Shaunak A. (Virginia Tech, 2024-11-11)
    In this thesis, we explore methods to enable assistive robotic arms mounted on wheelchairs to assist disabled users with their daily activities. To effectively aid users, these robots must recognize a variety of tasks and provide intuitive control mechanisms. We focus on developing techniques that allow these assistive robots to learn diverse tasks, manipulate different types of objects, and simplify user control of these complex, high-dimensional systems. This thesis is structured around three key contributions. First, we introduce a method for assistive robots to autonomously learn complex, high-dimensional behaviors in a given environment and map them to a low-dimensional joystick interface without human demonstrations. Through controlled experiments and a user study, we show that this approach outperforms systems based on human-demonstrated actions, leading to faster task completion compared to industry-standard baselines. Second, we improve the efficiency of reinforcement learning for robotic manipulation tasks by introducing a waypoint-based algorithm. This approach frames task learning as a sequence of multi-armed bandit problems, where each bandit problem corresponds to a waypoint in the robot's trajectory. We introduce an approximate posterior sampling solution that builds the robot's motion one waypoint at a time. Our simulations and real-world experiments show that this approach achieves faster learning than state-of-the-art baselines. Finally, to address the challenge of manipulating a variety of objects, we introduce RIgid-SOft (RISO) grippers that combine soft-switchable adhesives with standard rigid grippers and propose a shared control framework that automates part of the grasping process. The RISO grippers allow users to manipulate objects using either rigid or soft grasps, depending on the task. Our user study reveals that, with the shared control framework and RISO grippers, users were able to grasp and manipulate a wide range of household objects effectively. The findings from this research emphasize the importance of integrating advanced learning algorithms and control strategies to improve the capabilities of assistive robots in helping users with their daily activities. By exploring different directions within the domain of assistive robotics, this thesis contributes to the development of methods that enhance the overall functionality of assistive robotic arms.
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    Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction
    Mehta, Shaunak A.; Losey, Dylan P. (ACM, 2023)
    Humans can leverage physical interaction to teach robot arms. This physical interaction takes multiple forms depending on the task, the user, and what the robot has learned so far. State-of-the-art approaches focus on learning from a single modality, or combine multiple interaction types by assuming that the robot has prior information about the human?s intended task. By contrast, in this paper we introduce an algorithmic formalism that unites learning from demonstrations, corrections, and preferences. Our approach makes no assumptions about the tasks the human wants to teach the robot; instead, we learn a reward model from scratch by comparing the human?s inputs to nearby alternatives. We first derive a loss function that trains an ensemble of reward models to match the human?s demonstrations, corrections, and preferences. The type and order of feedback is up to the human teacher: we enable the robot to collect this feedback passively or actively. We then apply constrained optimization to convert our learned reward into a desired robot trajectory. Through simulations and a user study we demonstrate that our proposed approach more accurately learns manipulation tasks from physical human interaction than existing baselines, particularly when the robot is faced with new or unexpected objectives.