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The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.
On Jan 12, Michael Przystupa —a PhD Candidate at the University of Alberta — presented “Advances in Data Driven Teleoperation: A synthesis of previous works & our own" at the AI Seminar.
Abstract:
Data-driven teleoperation research investigates the application of machine learning models for mapping interactions between control interfaces (e.g., a joystick) and robotic arms to simplify human-robotic interactions. There is a real-world utility for such AI systems in assistive robotics, where people living with a disability rely on robots to complete daily tasks (e.g., opening doors, brushing teeth, etc.). Existing mappings involve cycling between controlling subsets of a robot’s pose. These can be tedious because they can require switching the control mode anywhere from 30 to 60 times to accomplish daily tasks. Data-driven mappings simplify teleoperation by mapping a user's inputs directly to desired robot motions, eliminating the need to switch the control mode.
This presentation covers previous works investigating data aspects of such systems, including data collection procedures (supervised vs unsupervised) and choice of data modalities (robotic proprioceptive information, images, text, and belief). The latter half of the presentation discusses Przystupa's own work explicitly enforcing teleoperation properties and briefly includes incorporating such models with shared autonomy systems. Overall, viewers can expect to learn how to build data-driven teleoperation systems and discover existing research in this application of machine learning.
Watch the full presentation below:
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