Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers.
and Shah, Kunal and Miller, Lee E and Cotton, R
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.
Evaluation of three pose estimation methods on infant videos shows Sapiens best for 2D consistency and SAM 3D Body best for 3D kinematic reconstruction with 19-28 mm errors, plus proof-of-concept for distinguishing motor patterns.
citing papers explorer
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Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control
Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers.
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PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.
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Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants
Evaluation of three pose estimation methods on infant videos shows Sapiens best for 2D consistency and SAM 3D Body best for 3D kinematic reconstruction with 19-28 mm errors, plus proof-of-concept for distinguishing motor patterns.