6 accepted papers
Long-horizon routing tasks of deformable linear objects (DLOs), such as cables and ropes, are common in industrial assembly lines and everyday life. These tasks are particularly challenging because they require robots to manipulate DLO with long-horizon planning and reliable skill execution. Success…
Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that excel at mapping language instructions to actions (L2A). However, this unidirectional training paradigm often produces policies that can execute tasks without deeper contextual understanding, ther…
The language-guided robot grasping task requires a robot agent to integrate multimodal information from both visual and linguistic inputs to predict actions for target-driven grasping. While recent approaches utilizing Multimodal Large Language Models (MLLMs) have shown promising results, their exte
Tasks involving deformable linear objects (DLOs) are prevalent in daily life but pose significant challenges due to their infinite degrees of freedom and underactuated nature. Frequent contact between DLOs and surrounding objects with unknown physical parameters, such as friction, further complicate
Manipulation of deformable Linear objects (DLOs), including iron wire, rubber, silk, and nylon rope, is ubiquitous in daily life. These objects exhibit diverse physical properties, such as Young’s modulus and bending stiffness. Such diversity poses challenges for developing generalized manipulation…
Robot manipulation of rope-like objects is an interesting problem with some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials.