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Award ID contains: 1944069

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  1. ; ; ; ; ; (, IEEE)
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  2. ; ; ; ; (, IEEE)
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  3. ; ; ; ; (, 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM))
    This paper presents the Pinch Sensor, an elastic input device to sense the fine motion and pinch force of the index finger and thumb - the two most used digits of human hands for in-hand object manipulation skills. In addition to open and close, the device would allow a user to control a robotic or simulated two-finger hand to reorient an object in three different ways and their combinations. A unique design of elastic sensing provides the users a high degree of perception resolution, as well as the sensation of holding an object with a certain level of stiffness between the index finger and thumb. These characteristics help the users to fine control the pinch force while carrying out manipulation skills. The design features a small size that allows it to be integrated to a handheld controller. Commonly available off-the-shelf components for consumer electronics are used to achieve affordability and reliability. 
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  4. ; ; (, IFAC-PapersOnLine)
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  5. ; ; (, IFAC-PapersOnLine)
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  6. ; ; (, ASME Letters in Dynamic Systems and Control)
    null (Ed.)
    Abstract This work discusses a crowdsourced learning scheme for robot physical intelligence. Using a large amount of data from crowdsourced mentors, the scheme allows robots to synthesize new physical skills that are never demonstrated or only partially demonstrated without heavy re-training. The learning scheme features a data management method to sustainably manage continuously collected data and a growing knowledge library. The method is validated using a simulated challenge of solving a bottle puzzle. The learning scheme aims at realizing ubiquitous robot learning of physical skills and has the potential of automating many demanding tasks that are currently hard to robotize. 
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  7. ; ; (, International Journal of Intelligent Robotics and Applications)
    null (Ed.)
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