13 accepted papers
Language-Conditioned Manipulation (LCM) facilitates human-robot interaction via Behavioral Cloning (BC), which learns control policies from human demonstrations and serves as a cornerstone of embodied AI. Overcoming compounding errors in sequential action decisions remains a central challenge to imp
Flexible needles provide enhanced adaptability for navigating puncture pathways and avoiding obstacles when compared to conventional rigid needles. However, developing a three dimensional (3D) curved path for flexible needle is challenging, particularly in achieving both effective obstacle avoidance…
Language-conditioned manipulation (LCM) couples vision, language, and control to enable natural instruction following, making it a promising direction in embodied AI research. Researchers have combined imitation learning (IL) with reinforcement learning (RL), where IL provides sample-efficient initi
The unique tilt-servo mechanism of the tilt-rotor unmanned aerial vehicle (UAV) facilitates seamless transitions between multi-rotor and fixed-wing modes, enhancing both flexibility and maneuverability. However, traditional modeling methods, which treat each flight mode independently, fail to provid
Unmanned aerial vehicles equipped with modern vision algorithms are crucial for missions such as reconstruction and target acquisition. However, when deployed in the field, undulating terrain can cause significant fluctuations in image scale and degrade the performance of vision algorithms. Instead
Flexible needles provide enhanced adaptability for navigating puncture pathways and avoiding obstacles when compared to conventional rigid needles. However, developing a three dimensional (3D) curved path for flexible needle is challenging, particularly in achieving both effective obstacle avoidance
Multimodal Emotion Recognition in Conversations (ERC) aims to identify the emotions conveyed by each utterance in a conversational video. Current efforts encounter challenges in balancing intra- and inter-speaker context dependencies when tackling intra-modal interactions. This balance is vital as i…
Hysteresis has posed significant challenges to the modeling and control of flexible endoscopic robots, which impedes the advancement of automated endoscopic operation. Despite numerous hysteresis modeling approaches aimed at improving accuracy, there are still several unresolved issues, such as inap…
Highly accurate sensor extrinsic calibration is critical for data fusion from multiple sensors, such as camera and Inertial Measurement Unit (IMU) sensor suit. A pre-calibrated extrinsics, however, may no longer be accurate due to external disturbances, e.g., vehicle vibration, which will lead to si
The crux of long-term tracking lies in the difficulty of tracking the target with discontinuous moving caused by out-of-view or occlusion. Existing long-term tracking methods follow two typical strategies. The first strategy employs a local tracker to perform smooth tracking and uses another re-dete…
Bounding box regression is a very effective method to improve the localization accuracy of object detection. Recently, the IoU-based regression losses have been widely used in object detection algorithms. However, we observe that they degenerate seriously in the late training period, leading to slow…
As a research hotspot of computer vision, crowd counting methods have achieved success in natural images. But crowd counting in aerial images are rarely explored, and existing methods do not perform well because of the higher resolution, smaller object scale and more complex scene. Therefore, this p…
Most existing trackers based on deep learning perform tracking in a holistic strategy, which aims to learn deep representations of the whole target for localizing the target. It is arduous for such methods to track targets with various appearance variations. To address this limitation, another type…