Author name cluster

Tyler Frasca

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4 papers

1 author row

AAAI Conference 2021 Conference Paper

Enabling Fast Instruction-Based Modification of Learned Robot Skills

Tyler Frasca
Bradley Oosterveld
Meia Chita-Tegmark
Matthias Scheutz

Much research effort in HRI has focused on how to enable robots to learn new skills from observations, demonstrations, and instructions. Less work, however, has focused on how skills can be corrected if they were learned incorrectly, adapted to changing circumstances, or generalized/specialized to different contexts. In this paper, a skill modification framework is introduced that allows users to modify a robot’s stored skills quickly through instructions to (1) reduce inefficiencies, (2) fix errors, and (3) enable generalizations, all in a way for modified skills to be immediately available for task performance. A thorough evaluation of the implemented framework shows the operation of the algorithms integrated in a cognitive robotic architecture on different fully autonomous robots in various HRI case studies. An additional online HRI user study verifies that subjects prefer to quickly modify robot knowledge in the way we proposed in the framework.

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IJCAI Conference 2018 Conference Paper

Recursive Spoken Instruction-Based One-Shot Object and Action Learning

Matthias Scheutz
Evan Krause
Bradley Oosterveld
Tyler Frasca
Robert Platt

Learning new knowledge from single instructions and being able to apply it immediately is highly desirable for artificial agents. We provide the first demonstration of spoken instruction-based one-shot object and action learning in a cognitive robotic architecture and briefly discuss the architectural modifications required to enable such fast learning, demonstrating the new capabilities on a fully autonomous robot.

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AAMAS Conference 2017 Conference Paper

Spoken Instruction-Based One-Shot Object and Action Learning in a Cognitive Robotic Architecture

Matthias Scheutz
Evan Krause
Brad Oosterveld
Tyler Frasca
Robert Platt

Learning new knowledge from single instructions and being able to apply it immediately is a highly desirable capability for artificial agents. We provide the first demonstration of spoken instructionbased one-shot object and action learning in a cognitive robotic architecture and discuss the modifications to several architectural components required to enable such fast learning, demonstrating the new capabilities on two different fully autonomous robots. CCS Concepts •Human-centered computing → Natural language interfaces; •Computing methodologies → Online learning settings;

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AAAI Conference 2016 Conference Paper

A Comparison of Supervised Learning Algorithms for Telerobotic Control Using Electromyography Signals

Tyler Frasca
Antonio Sestito
Craig Versek
Douglas Dow
Barry Husowitz
Nate Derbinsky

Human Computer Interaction (HCI) is central for many applications, including hazardous environment inspection and telemedicine. Whereas traditional methods of HCI for teleoperating electromechanical systems include joysticks, levers, or buttons, our research focuses on using electromyography (EMG) signals to improve intuition and response time. An important challenge is to accurately and efﬁciently extract and map EMG signals to known position for real-time control. In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing speciﬁc arm positions. We present results from ofﬂine analysis, as well as end-to-end operation using a robotic arm.

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