TY - GEN
T1 - Anytime, Anywhere
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
AU - Weigend, Fabian C.
AU - Sonawani, Shubham
AU - Drolet, Michael
AU - Amor, Heni Ben
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This work devises an optimized machine learning approach for human arm pose estimation from a single smart-watch. 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 solutions, i.e., multimodal pose distributions. Combining estimated arm postures with speech recognition, we turn the smartwatch into a ubiquitous, low-cost and versatile robot control interface. We demonstrate in two use-cases that this intuitive control interface enables users to swiftly intervene in robot behavior, to temporarily adjust their goal, or to train completely new control policies by imitation. Extensive experiments show that the approach results in a 40% reduction in prediction error over the current state-of-the-art and achieves a mean error of 2.56 cm for wrist and elbow positions.
AB - This work devises an optimized machine learning approach for human arm pose estimation from a single smart-watch. 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 solutions, i.e., multimodal pose distributions. Combining estimated arm postures with speech recognition, we turn the smartwatch into a ubiquitous, low-cost and versatile robot control interface. We demonstrate in two use-cases that this intuitive control interface enables users to swiftly intervene in robot behavior, to temporarily adjust their goal, or to train completely new control policies by imitation. Extensive experiments show that the approach results in a 40% reduction in prediction error over the current state-of-the-art and achieves a mean error of 2.56 cm for wrist and elbow positions.
UR - http://www.scopus.com/inward/record.url?scp=85182523823&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182523823&partnerID=8YFLogxK
U2 - 10.1109/IROS55552.2023.10341624
DO - 10.1109/IROS55552.2023.10341624
M3 - Conference contribution
AN - SCOPUS:85182523823
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3811
EP - 3818
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 October 2023 through 5 October 2023
ER -