Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Christen, Sammy"'
We present EgoHDM, an online egocentric-inertial human motion capture (mocap), localization, and dense mapping system. Our system uses 6 inertial measurement units (IMUs) and a commodity head-mounted RGB camera. EgoHDM is the first human mocap system
Externí odkaz:
http://arxiv.org/abs/2409.00343
We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object trajectory. Due to the challenges in controlling a humanoid with dexterous hands, prior methods often use a disembodied hand and only consider
Externí odkaz:
http://arxiv.org/abs/2407.11385
Autor:
Albaba, Mert, Christen, Sammy, Gebhardt, Christoph, Langarek, Thomas, Black, Michael J., Hilliges, Otmar
Reinforcement Learning has achieved significant success in generating complex behavior but often requires extensive reward function engineering. Adversarial variants of Imitation Learning and Inverse Reinforcement Learning offer an alternative by lea
Externí odkaz:
http://arxiv.org/abs/2406.08472
Human hands possess the dexterity to interact with diverse objects such as grasping specific parts of the objects and/or approaching them from desired directions. More importantly, humans can grasp objects of any shape without object-specific skills.
Externí odkaz:
http://arxiv.org/abs/2403.19649
Autor:
Christen, Sammy, Hampali, Shreyas, Sener, Fadime, Remelli, Edoardo, Hodan, Tomas, Sauser, Eric, Ma, Shugao, Tekin, Bugra
Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furthermore, generalization to unseen objects is hindered by the limited sc
Externí odkaz:
http://arxiv.org/abs/2403.17827
Vision-based human-to-robot handover is an important and challenging task in human-robot interaction. Recent work has attempted to train robot policies by interacting with dynamic virtual humans in simulated environments, where the policies can later
Externí odkaz:
http://arxiv.org/abs/2311.05599
We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-object interactions, a comprehensive physics-based approach remains a chal
Externí odkaz:
http://arxiv.org/abs/2309.07907
Autor:
Zhang, Hui, Christen, Sammy, Fan, Zicong, Zheng, Luocheng, Hwangbo, Jemin, Song, Jie, Hilliges, Otmar
We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary
Externí odkaz:
http://arxiv.org/abs/2309.03891
Autor:
Christen, Sammy, Yang, Wei, Pérez-D'Arpino, Claudia, Hilliges, Otmar, Fox, Dieter, Chao, Yu-Wei
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has made significant progress in training robot agents in simulated environ
Externí odkaz:
http://arxiv.org/abs/2303.17592
Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user
Externí odkaz:
http://arxiv.org/abs/2209.12660