6 accepted papers
Tele-homecare has become a promising approach to meet the growing demand for elderly and disability care. In such a context, ensuring human-robot interaction safety during teleoperation poses a critical challenge. Existing teleoperation control approaches focus solely on the robot’s end-effector tra
This study presents a safety-aware shared control strategy that combines proximity sensing and force guidance to achieve precise and stable teleoperation under dynamic interference. Based on the sensing information of the proximity sensor, a safety-aware controller is designed to enable the manipula
"Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based inductive bias like the human recognition process. Motivated by this, several part-based recognition models have been…
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training. In this paper, we propose a frustratingly simple but effective step-wise learning framework to gradually enhance the capability of the model in detecting al…
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detection that rely on the availability of abundant training samples…
Deep networks have shown remarkable results in the task of object detection. However, their performance suffers critical drops when they are subsequently trained on novel classes without any sample from the base classes originally used to train the model. This phenomenon is known as catastrophic for…