10 accepted papers
Neural network–based visuomotor policies enable robots to perform manipulation tasks but remain susceptible to perceptual attacks. For example, conventional 2D adversarial patches are effective under fixed-camera setups, where appearance is relatively consistent; however, their efficacy often dimini…
Robotic manipulation involves kinematic and semantic transitions that are inherently coupled via underlying actions. However, existing approaches plan within either semantic or latent space without explicitly aligning these cross-modal transitions. To address this, we propose CLaD, a framework that
Cross-view multi-object tracking (CVMOT) aims to simultaneously track a group of objects over time in each view and to associate the same objects across different views. This task is essential for swarm monitoring and has attracted considerable attention in the literature. However, most existing dat
Rapidly-exploring random trees (RRTs) have been widely adopted for robot motion planning due to their robustness and theoretical guarantees. However, existing RRT-based planners require explicit goal configurations specified as numerical joint angles, while many practical applications provide goal s
Mobile robot navigation in crowded indoor environments is a challenging task due to the limited sensing capabilities of onboard sensors. In this study, we propose a mobile robot navigation framework that utilizes external CCTV data to address the limitations of local sensors in a crowded environment
LiDAR-camera calibration plays a crucial role in autonomous driving. However, operation-induced factors such as physical vibrations and temperature variations degrade the pre-deployment calibration accuracy, leading to the environmental perception performance deterioration. Recent recalibration meth…
"This paper presents a novel approach for reconstructing dynamic radiance fields from monocular videos. We integrate kinematics with dynamic radiance fields, bridging the gap between the sparse nature of monocular videos and the real-world physics. Our method introduces the kinematic field, capturin…
We present "SemCity" a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object synthetic indoor scenes or synthetic outdoor scenes while the generation of real-world outdoor scenes is rarely addressed. In this…
"A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset. However, obtaining ground-truth flows from a target video requires a tremendous effort. This paper proposes a practical fine tuning method to adapt a…
Vision aided dynamic exploration on bipedal robots poses an integrated challenge for perception and control. Rapid walking motions as well as the vibrations caused by the landing-foot contact-force introduce critical uncertainty in the visual-inertial system, which can cause the robot to misplace it…