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Title: IHSR: A Framework Enables Robots to Learn Novel Hand Signals From a Few Samples
This project introduces a framework to enable robots to recognize human hand signals, a reliable and feasible device-free means of communication in many noisy environments such as construction sites and airport ramps, to facilitate efficient human-robot collaboration. Various hand signal systems are accepted in many small groups for specific purposes, such as Marshalling on airport ramps and construction site crane operations. Robots must be robust to unpredictable conditions, including various backgrounds and human appearances, an extreme challenge imposed by open environments. To address these challenges, we propose Instant Hand Signal Recognition (IHSR), a learning-based framework with world knowledge of human gestures embedded, for robots to learn novel hand signals in a few samples. It also offers robust zero-shot generalization to recognize learned signals in novel scenarios. Extensive experiments show that our IHSR can learn a novel hand signal in only 50 samples, which is 30+ times more efficient than the state-of-the-art method. It also demonstrates a robust zero-shot generalization for deploying a learned model in unseen environments to recognize hand signals from unseen human users.  more » « less
Award ID(s):
2245607
PAR ID:
10598004
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISSN:
2159-6255
ISBN:
979-8-3503-5536-9
Page Range / eLocation ID:
959 to 965
Subject(s) / Keyword(s):
Zero-shot Generalization, Gesture Recognition, Hand signals, Cross-modality Embedding
Format(s):
Medium: X
Location:
Boston, MA, USA
Sponsoring Org:
National Science Foundation
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