8 accepted papers
6-DoF object grasping is a crucial skill for embodied intelligent robots. Previous methods often rely on large-scale networks for feature extraction, followed by grasp pose prediction, which increases the network
Object pose estimation is a key task for embodied robots, enabling them to interact with objects effectively. Category-level object pose estimation provides a way for robots to estimate the pose of unknown objects. However, estimating object pose from point clouds alone remains challenging. In this
This letter presents an improved multidimensional filter bank canonical correlation analysis (FBCCA) method for the brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP). This is a training-free SSVEP recognition method based on FBCCA, which integrates partial
Safe reinforcement learning (RL) aims to learn policy while also ensuring the safety constraints. An increasingly common approach is to design a safety filter based on control barrier function (CBF) or high order control barrier function (HOCBF) for the RL policy. A quadratic programming (QP) is the
Category-level object pose estimation is an important task in computer vision. Some prior methods based on assumptions often struggle with drastic changes in object appearance. To address this challenge, we propose a new method for object pose estimation based on object-adaptive keypoints. In this p…
While most current RGB-D-based category-level object pose estimation methods achieve strong performance, they face significant challenges in scenes lacking depth information. In this paper, we propose a novel category-level object pose estimation approach that relies solely on RGB images. This metho
Although there has been significant progress in category-level object pose estimation in recent years, there is still considerable room for improvement. In this paper, we propose a novel transformer-based category-level 6D pose estimation method called CatFormer to enhance the accuracy pose estimat…
In this letter, a novel grasp detection neural network Squeeze-and-Excitation ResUNet (SE-ResUNet) is developed, where the residual block with the channel attention is integrated. The proposed framework can not only generate the grasp pose from the RGB-D images, but also predict the quality score of