Core Concepts
AdaFold optimizes cloth folding trajectories using feedback-loop manipulation and semantic descriptors.
Abstract
The article introduces AdaFold, a model-based framework for optimizing cloth folding trajectories through feedback-loop manipulation. It leverages semantic descriptors from pre-trained visual-language models to enhance the particle representation of cloth. The experiments demonstrate AdaFold's ability to adapt folding trajectories to cloths with varying physical properties and generalize from simulation to real-world execution. The content is structured into sections covering Introduction, Related Work, Problem Formulation, Cloth Perception, Trajectory Optimization, Implementation Details, Experimental Results, Ablation Study, Real World Experiments, and Semantic Cloth Representation.
Stats
AdaFold는 옷을 접는 궤적을 최적화하기 위한 모델 기반 피드백 루프 프레임워크입니다.
AdaFold는 시뮬레이션에서 실제 세계로의 일반화를 통해 옷의 물리적 특성이 다른 옷에 대한 접는 궤적을 적응시키는 능력을 시연합니다.
Quotes
"AdaFold adapts folding trajectories to cloths with varying physical properties and generalizes from simulated training to real-world execution."
"Our experiments validate the hypothesis that AdaFold adapts folding trajectories to variations in physical properties, positions, and sizes of cloths."