7 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
Adversarial object rearrangement in the real world (e.g., previously unseen or oversized items in kitchens and stores) could benefit from understanding task scenes, which inherently entail heterogeneous components such as current objects, goal objects, and environmental constraints. The semantic rel…
When robots retrieve specific objects from cluttered scenes, such as home and warehouse environments, the target objects are often partially occluded or completely hidden. Robots are thus required to search, identify a target object, and successfully grasp it. Preceding works have relied on pre-trai…
"Instance segmentation with unseen objects is a challenging problem in unstructured environments. To solve this problem, we propose a robot learning approach to actively interact with novel objects and collect each object’s training label for further fine-tuning to improve the segmentation model per…