12 accepted papers
Motion detection in 3D LiDAR is crucial for autonomous systems. While deep learning dominates Moving Object Segmentation (MOS), the potential of learning-free approaches remains underexplored. Unlike problems like semantic segmentation, motion can be explicitly modeled, potentially enabling efficien
Accurate temporal segmentation of human actions is critical for intelligent robots in collaborative settings, where a precise understanding of sub-activity labels and their temporal structure is essential. However, the inherent noise in both human pose estimation and object detection often leads to
The ability to follow people can benefit the human-robot interaction of mobile robots. This work proposes a hybrid human tracking system for human following robots, integrating sensor fusion of Ultra-Wideband (UWB) and monocular visual positioning to enhance tracking accuracy and precision. At the s…
Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world
Vital signs such as heart rate, oxygen saturation, and blood pressure are crucial information for healthcare workers to identify clinical deterioration of ward patients. Currently, medical devices monitor these vital signs and trigger alarms when the vital signs are not in the normal ranges based on…
Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior experience of successful motion plans in a neural network. However, this “neural moti…
Human activities recognition is an important task for an intelligent robot, especially in the field of human-robot collaboration, it requires not only the label of sub-activities but also the temporal structure of the activity. In order to automatically recognize both the label and the temporal stru…
The need for comprehensive telemedicine solutions is becoming increasingly relevant due to challenges associated with the ageing population, the increasing shortage of health-care providers, and, more recently, the global pandemic. Existing solutions primarily focus on, e.g., electronic medical reco…
This paper proposes a novel dictionary learning approach to detect event anomalities using skeletal information extracted from RGBD video. The event action is represented as several latent action atoms and composed of latent spatial and temporal attributes. We aim to construct a network able to lear…
We present a novel representation of dynamic scenes perceived by moving agents. This type of environments require constant updates of the map information that uses conventional geometric representations due to the changing relative position of dynamic object to the static scene. We show that this ch…
Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between t…
We propose an object-centric framework to label and predict human manipulation actions from observations of the object trajectories in 3D space. The goal is to lift the low-level sensor observation to a context specific human vocabulary. The low-level visual sensory input from a depth camera is proc…