Abstract-We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-theloop systems further in the discussion.
Assistive robotic manipulators have the potential to improve the lives of people with motor impairments. They can enable individuals to perform activities such as pick-and-place tasks, opening doors, pushing buttons, and can even provide assistance in personal hygiene and feeding. However, robotic arms often have more degrees of freedom (DoF) than the dimensionality of their control interface, making them challenging to use—especially for those with impaired motor abilities. Our research focuses on enabling the control of high-DoF manipulators to motor-impaired individuals for performing daily tasks. We make use of an individual’s residual motion capabilities, captured through a Body-Machine Interface (BMI), to generate control signals for the robotic arm. These low-dimensional controls are then utilized in a shared-control framework that shares control between the human user and robot autonomy. We evaluated the system by conducting a user study in which 6 participants performed 144 trials of a manipulation task using the BMI interface and the proposed shared-control framework. The 100% success rate on task performance demonstrates the effectiveness of the proposed system for individuals with motor impairments to control assistive robotic manipulators.
We propose a generalizable natural language interface that allows users to provide corrective instructions to an assistive robotic manipulator in real-time. This work is motivated by the desire to improve collaboration between humans and robots in a home environment. Allowing human operators to modify properties of how their robotic counterpart achieves a goal on-the-fly increases the utility of the system by incorporating the strengths of the human partner (e.g. visual acuity and environmental knowledge). This work is applicable to users with and without disability. Our natural language interface is based on the distributed correspondence graph, a probabilistic graphical model that assigns semantic meaning to user utterances in the context of the robot’s environment and current behavior. We then use the desired corrections to alter the behavior of the robotic manipulator by treating the modifications as constraints on the motion generation (planning) paradigm. In this paper, we highlight four dimensions along which a user may wish to correct the behavior of his or her assistive manipulator. We develop our language model using data collected from Amazon Mechanical Turk to capture a comprehensive sample of terminology that people use to describe desired corrections. We then develop an end-to-end system using open-source speech-to-text software and a Kinova Robotics MICO robotic arm. To demonstrate the efficacy of our approach, we run a pilot study with users unfamiliar with robotic systems and analyze points of failure and future directions.
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