key: Wearable Motion Capture
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iRoCo: Intuitive Robot Control From Anywhere Using a Smartwatch
Summary: This paper introduces a framework for ubiquitous human-robot collaboration using a single smartwatch and smartphone. By integrating probabilistic differentiable filters, we optimize a combination of precise robot control and unrestricted user movement from ubiquitous devices. The system is available as WearMocap. We demonstrate and evaluate the effectiveness of WearMoCap in practical teleoperation and drone…
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Ubiquitous Robot Control Through Multimodal Motion Capture Using Smartwatch and Smartphone Data
Presented as an interactive poster with motion capture game at the ICRA24 Workshop – Advancing wearable devices and applications through novel design, sensing, actuation, and AI. Abstract: We present an open-source library for seamless robot control through motion capture using smartphones and smartwatches. Our library features three modes: Watch Only Mode, enabling control with a…
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Probabilistic Differentiable Filters Enable Ubiquitous Robot Control With Smartwatches
Received a monetary prize and got selected for a 10min spotlight presentation! Thanks very much to the organizers of the Differential Robotics Workshop at IROS 23. Ubiquitous robot control and human-robot collaboration using smart devices poses a challenging problem primarily due to strict accuracy requirements and sparse information. This paper presents a novel approach that…
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Anytime, Anywhere: Human Arm Pose From Smartwatch Data for Ubiquitous Robot Control and Teleoperation
Nominated for the RoboCup Best Paper Award! (Top 6% of IROS23) This work devises an optimized machine learning approach for human arm pose estimation from a single smartwatch. Our approach results in a distribution of possible wrist and elbow positions, which allows for a measure of uncertainty and the detection of multiple possible arm posture…