Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Burschka, Darius"'
Autor:
Xing, Hao, Burschka, Darius
Understanding human activity is a crucial aspect of developing intelligent robots, particularly in the domain of human-robot collaboration. Nevertheless, existing systems encounter challenges such as over-segmentation, attributed to errors in the up-
Externí odkaz:
http://arxiv.org/abs/2410.07917
Autor:
Xing, Hao, Burschka, Darius
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 automatica
Externí odkaz:
http://arxiv.org/abs/2410.07912
We propose a method to systematically represent both the static and the dynamic components of environments, i.e. objects and agents, as well as the changes that are happening in the environment, i.e. the actions and skills performed by agents. Our ap
Externí odkaz:
http://arxiv.org/abs/2409.08853
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
Externí odkaz:
http://arxiv.org/abs/2407.18834
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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 pla
Externí odkaz:
http://arxiv.org/abs/2311.08345
Publikováno v:
2022 IEEE-RAS International Conference on Humanoid Robots (Humanoids)
When a humanoid robot performs a manipulation task, it first makes a model of the world using its visual sensors and then plans the motion of its body in this model. For this, precise calibration of the camera parameters and the kinematic tree is nee
Externí odkaz:
http://arxiv.org/abs/2311.08338
Supervised learning depth estimation methods can achieve good performance when trained on high-quality ground-truth, like LiDAR data. However, LiDAR can only generate sparse 3D maps which causes losing information. Obtaining high-quality ground-truth
Externí odkaz:
http://arxiv.org/abs/2207.06351
Autor:
Xing, Hao, Burschka, Darius
In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask. However, in most existing Graph Convolutional Networ
Externí odkaz:
http://arxiv.org/abs/2207.05493
This paper propose a novel dictionary learning approach to detect event action using skeletal information extracted from RGBD video. The event action is represented as several latent atoms and composed of latent spatial and temporal attributes. We pe
Externí odkaz:
http://arxiv.org/abs/2109.02376
We present an approach for optical navigation in unstructured, dynamic railroad environments. We propose a way how to cope with the estimation of the train motion from sole observations of the planar track bed. The occasional significant occlusions d
Externí odkaz:
http://arxiv.org/abs/2007.03409