Deformable Object

Deformable object manipulation research focuses on enabling robots to effectively interact with objects like cloth, rope, and dough, which pose significant challenges due to their infinite degrees of freedom and complex dynamics. Current research emphasizes developing robust models, often employing neural networks (e.g., graph neural networks, neural radiance fields, and diffusion models) and advanced simulation techniques (e.g., material point methods) to accurately predict and control object deformations. This field is crucial for advancing robotics in various sectors, including manufacturing, healthcare, and domestic assistance, by enabling automation of tasks previously requiring human dexterity. The development of large-scale datasets and standardized benchmarks is also a key focus to facilitate more reliable comparisons and accelerate progress.